What is video object detection? How does it work?
Video Object Detection is a computer vision task that involves identifying and tracking objects of interest within a video sequence. Video object detection tasks incorporate temporal information to keep track of the objects across different video sequence frames. The primary objective here is to capture objects' spatial and temporal attributes and analyze the movements of different objects across the video.
There are several steps involved in video object detection, as follows:
Image Object Detection: Object detection is performed on individual video frames.
Temporal Information Tracking: Next, to maintain temporal information, techniques such as optical flow and motion estimation are used to predict the positions of the objects in the following frames based on previous frames.
Object Association: The next task is associating objects detected in the current frame with those in the previous frames using object association algorithms.
Motion Tracking: Once all the objects are associated, tracking algorithms are used to update the positions and velocities of each object as the video progresses. These algorithms must be robust to object occlusion and abrupt motion changes.
The final output of the system comprises positions and class labels of the detected objects across the video, which is often obtained after several post-processing steps, such as refining the object boundaries and handling the false positive detections.
The importance of video object detection
The advancement of machine learning and AI research over the past few years has paved the way to build efficient video object detection algorithms that enable AI systems to comprehend and interpret the dynamic visual information in videos. The ability to capture temporal information is crucial for several applications.
Firstly, in the surveillance domain, video object detection helps identify and track suspicious intruders and activities in real time.
In autonomous vehicles, video object detection ensures safe navigation by recognizing and tracking vehicles, road signs, pedestrians, and other obstacles.
Similarly, in robotics, video object detection facilitates the perception of the surrounding environment, enabling robots to interact intelligently within complex spaces.
In healthcare, this technology assists in analyzing medical imaging data and identifying and tracking anomalies, tumors, and other critical features, thereby contributing to more accurate diagnostics and treatment planning.
In retail, video object detection helps understand consumer behavior, enabling tracking of customer movements and preferences, leading to improved marketing strategies and personalized customer experiences.
Overall, video object detection algorithms significantly enhance the ability of machine learning and AI systems to interpret and respond to real-world visual information, thus empowering various industries with advanced capabilities for automation and decision-making.
Examples of video object detection models
Video object detection has witnessed a remarkable transformation over the years, primarily driven by advancements in algorithms and techniques. This evolution began with traditional methods such as background subtraction and optical flow, which laid the groundwork for understanding and interpreting video data. As technology progressed, deep learning methods like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) emerged, offering more sophisticated and accurate ways to detect and track objects in videos. This section delves into the key algorithms and techniques that have shaped the landscape of video object detection, exploring their principles, applications, and the challenges they address.
Traditional video object detection algorithms
Background subtraction is a prevalent traditional method for performing video object detection. Its main objective is to focus on moving or non-stationary foreground objects by eliminating the stationary or slowly changing background. The main principle here is that the background of a scene remains relatively consistent over time, which makes it easy to distinguish objects of interest in the foreground.
The first step in the process is to construct a model that can accurately represent the background of the video using statistical methods such as averaging pixel values or using Gaussian mixture models. Next, each frame is analyzed against this model to check the pixel deviations between each frame and the background. Significantly differing pixels represent potentially moving objects; hence, they can be classified as foreground.
Several challenges, such as different lighting conditions, shadows, occlusions, noisy backgrounds, or abrupt changing of background, can make this technique difficult to implement. To address these and other issues, it is always a good idea to use adaptive modeling and some post-processing techniques to segregate the foreground better. However, it is difficult to promise an accurate output with this technique; hence, it is often used as a preprocessing step in the current advanced computer vision applications.
Optical flow is another traditional computer vision technique used to estimate the motion of objects in a video. This approach is based on the principle that the intensity of pixels remains constant across the frames. Therefore, analyzing the brightness patterns in consecutive frames allows tracking an object's motion in a scene. Technically, it calculates the displacement of pixels and generates a vector field for the motion of each pixel. Different techniques to compute the optical flow using mathematical models involve differential methods such as Lucas-Kanade and feature-based methods such as the Kanade-Lucas-Tomasi (KLT) tracker.
Optical flow is a widespread technique with applications across tasks such as object tracking and motion analysis. Its key advantage lies in its ability to track the trajectory of pixel intensity across successive video frames, making it especially useful for understanding the motion dynamics of objects without needing to identify them. However, the technique does have limitations. In scenarios with frequent occlusions—where objects are partially or fully obstructed from view—or when the objects move rapidly, the optical flow can struggle to provide accurate estimations. This is also the case when the visual scene is highly complex or patterns change abruptly, which can confuse the algorithm as it relies on the consistency of patterns over time. Therefore, this method is often used as a foundation in advanced tracking algorithms.
Deep learning models for video object detection
Convolutional Neural Networks (CNNs)
Convolutional Neural Networks are the foundational networks in deep learning for performing video object detection. They are proficient in extracting spatial features from individual frames and capturing temporal dependencies across subsequent frames. CNNs extract information through the convolution of kernels with the individual frames to identify objects. Temporal information is incorporated using techniques such as optical flow or RNNs, which analyze the motion of objects and track object trajectories across the video sequence. Therefore, using these two approaches, the algorithm detects and tracks objects. Usually, the detections are post-processed using techniques such as non-maximum suppression to refine the detections and improve tracking precision. Thus, CNNs play a pivotal role in applications of video object detection for understanding dynamic visual scenes.
Recurrent Neural Networks (RNNs)
Recurrent Neural Networks (RNNs) are instrumental in video object detection due to their ability to effectively capture temporal information in the video data. Since video comprises multiple frames, it is essential to model the sequential information and make sense of the object's movements over time. This is done with the help of memory cells (recurrent units) in the hidden layers, which remember the information from previous frames and use it to process the current frame. RNNs can also be combined with techniques such as attention mechanisms or long short-term memory cells to effectively capture the video's long-range dependencies and complex patterns. RNNs can be particularly useful for applications such as action recognition and combining them with models providing other capabilities they can be used for various applications such as surveillance and human-computer interaction.
You only look once (YOLO)
YOLO and its variants up to YOLO-v8 are some of the most popular and state-of-the-art real-time object detection algorithms. They divide the input image into a grid and predict the bounding boxes and class probabilities for each grid cell. For video object detection, grid formations and predictions are performed on individual video frames; the neural network is applied to the entire image. Forming a grid enables the algorithm to detect multiple objects of varying scales and sizes easily.
YOLO uses deep convolutional layers for feature extraction and employs techniques such as non-maximum suppression and anchor boxes to refine the detections and remove duplicate detections. YOLO comprises a unified architecture to capture spatial and temporal information; hence, it is suitable for video object detection. It is popular because of its fast processing time and real-time predictions, so it is widely used across domains such as autonomous driving and robotics.
Single shot multi-box detector (SSD)
SSD is another popular deep-learning framework used for real-time object detection. It applies the neural network to individual video frames like previous algorithms. However, it already has predefined bounding boxes of varying ratios and scales that apply to the single frame to detect objects.
So, each bounding box of predefined aspect ratio and scale will have its class probabilities. This approach allows SSD to capture objects of varying sizes in a single pass, making it suitable for real-time detection. In addition to detection, SSD can also track objects across different frames, making it suitable for video object detection. Like other algorithms, SSD also integrates non-maximum suppression techniques for refining the detections to enhance accuracy.
To summarize, the ability of SSD to detect objects of varying shapes and sizes in a single pass makes it suitable for real-time use and a valuable tool in applications for video surveillance, action recognition, traffic management, and retail.
The progression from traditional methods to advanced deep learning approaches in video object detection reflects the continuous pursuit of more efficient, accurate, and versatile computer vision solutions. These technologies enhance our understanding of video content and open new possibilities for innovation across various industries.
Challenges in Video Object Detection
Video Object Detection is a challenging task as it requires considering numerous factors and challenges that can arise from the complexity of the video data. Some of the critical challenges are mentioned below:
Real-time processing: Obtaining real-time performance for video object detection is challenging as the computational speed depends on the complexity and resolution of the video, which is uncertain to determine beforehand.
Occlusions: All the objects in the video may not be fully visible. They may often be occluded partially or fully by other objects or the environment, making it difficult to detect them accurately.
Motion blur: Motion blur can be caused if the objects move too fast or the camera is unstable. This can create a loss of details, making it difficult for the algorithm to detect and track them.
Scale variation: Objects may not maintain the same scale and size throughout the video. It may change based on the object's proximity to the camera across all the frames. Hence, handling these variations is a challenging aspect of the algorithms.
Background clutter: The clutter or noise in the object's background can interfere with the detection process, specifically when the background exhibits similar features to the object. This can lead to false positives or missed detections.
Illumination changes: The lighting conditions in terms of brightness, shadow, or glare can change across the video frames, affecting the appearance of the objects and making it challenging to maintain the detection accuracy.
Data association and tracking: The association and tracking of detected objects across multiple frames is challenging, significantly when the environmental conditions are changing and causing frequent occlusions or appearance changes.
Addressing these challenges requires experimenting with different preprocessing, modeling, and post-processing techniques tailored to specific tasks.
Data collection and pre-processing for video object detection
Part of addressing these challenges in video object detection starts with collecting and pre-processing data. When collecting and preprocessing data for video object detection, several key points should be considered to ensure the effectiveness and efficiency of the process:
Data Diversity: The data should be diverse; it should contain different scenarios and environments that pertain to the target application. Also, the greater the diversity, the better the generalizing capability of the model.
Annotation Consistency: Ensure consistent and accurate annotation across all frames and videos to provide reliable ground truth data for model training and evaluation.
Data Augmentation: Apply data augmentation techniques, such as flipping, rotation, or scaling, to increase the diversity and quantity of the training data, enhancing not only the model's robustness but also the generalization capability.
Video Quality: Collect high-quality videos with sufficient resolution to ensure that the objects of interest are visible and distinguishable, facilitating accurate annotation and model training.
Temporal Sampling: Consider the appropriate frame rate and temporal sampling frequency for video data collection, ensuring that the captured frames provide sufficient temporal information for effective object detection and tracking.
Noise Reduction: Implement preprocessing techniques, such as denoising and smoothing, to reduce noise and artifacts in the video data, enhancing the overall quality of the input data for the model.
Data Privacy and Ethics: Adhere to data privacy regulations and ethical guidelines when collecting and preprocessing video data, ensuring the data collection process is conducted responsibly and lawfully.
Best data labeling practices for video object detection
Fast annotation of similar class objects in video
While the data has been collected and preprocessed, the next step is to ensure that best annotation practices are followed. The more precise and accurate the annotations, the better the model output. Some of the must-follow best practices are:
Temporal Consistency: Ensure that objects are consistently annotated across consecutive frames, accurately capturing their movements and trajectories throughout the video sequence.
Frame-Level Labeling: Label each frame individually to ensure precise object localization and classification, facilitating accurate model training and evaluation.
Multiple Object Instances: Annotate all instances of the same object class within a frame, especially in cases where multiple instances of an object class appear simultaneously.
Handling Occlusions: Annotate partially occluded objects, indicating their visible portions within the bounding box to provide comprehensive information for the model.
Dynamic Object Changes: Adjust bounding boxes for objects that change shape or size dynamically within a video sequence, ensuring an accurate representation of their spatial dimensions over time.
Motion Trajectory Annotation: Annotate moving objects' trajectories, marking their positions across frames to enable accurate tracking and analysis of object movements.
Object Identification: Use unique identifiers or tags for individual objects within a video sequence to differentiate and track them accurately, ensuring distinct annotations for each object instance.
For efficient object detection, precise annotation is crucial. An advanced tool like Kili Technology's video annotation platform can facilitate this process. It has been redesigned with a more precise, more navigable timeline and adjustable propagation settings for annotations, essential for accurately tracking objects across frames.
Smart tracking feature on Kili
Interpolation and smart tracking features are also included to automatically annotate moving objects, saving time on manual annotation. While annotating, it's possible to adjust the span of annotated frames easily, providing flexibility and enhancing the quality of temporal annotations. These features support best practices by allowing detailed frame-by-frame analysis and adjustment, which is crucial for training accurate video object detection models.
Dive in to our new video annotation tool
Kili's data labeling platform just got a huge upgrade. Check it out and learn how we've just made video annotation much easier!
Quality Assurance in Video Annotation
Conducting a quality assurance check in video annotation is extremely important to ensure the implemented pipeline is error-free, generates good accuracy, and has a reasonable annotation speed vs accuracy tradeoff. Additionally, quality assurance is needed to ensure that all the ethical points are considered and that the tool/algorithm is safe to be sent to production. Here are some of the significant points to consider for the quality assurance test:
Quality Evaluation Metrics: Define and employ appropriate evaluation metrics, such as inter-annotator agreement, precision, recall, F1 score, and Intersection over Union, to assess the accuracy and consistency of annotations.
Annotation Time Benchmarking: Establish benchmarking standards for annotation time per frame or per video segment to gauge the efficiency and productivity of annotators.
Monitoring of Annotation Speed: Continuously monitor and track the annotation speed of individual annotators and the overall annotation team, identifying variations and promptly addressing potential bottlenecks or inefficiencies.
Feedback Integration: Integrate feedback mechanisms for annotators to report annotation speed and quality issues, fostering a culture of continuous improvement and learning within the annotation team.
Kili Technology provides an excellent feature for calculating consensus rules for object detection tasks for bounding boxes, polygons, and semantic segmentation. It considers each image as a set of pixels to be classified. The individual pixel can be classified into several non-exclusive categories or classes to represent different objects. The metric requires calculating all annotations' Intersection over the Union (IOU).
Calculation metrics for bounding box object detection tasks on Kili
If two bounding boxes overlap perfectly, their metric is 100%, whereas two distinct non-overlapping shapes will correspond to a metric of 0%. This method is particularly beneficial because it ensures accuracy for shapes of all sizes.
Additionally, Kili technology provides a super easy and convenient way to view quality insights through its analytics functionality. The Quality Insights tab on the dashboard of Kili will provide insights regarding consensus and honeypot metrics. It provides per-class and per-labeler information and a total agreement score for consensus and honeypot. Furthermore, one can also start one’s review and use graphs and scores to target the relevant assets.
If neither consensus nor honeypot is set in the project, then the Quality Insights tab shows the review score per labeler. These review scores are calculated by comparing the original label with its reviewed version. This is an important metric to assess how well a team is performing.
Productivity and quality review score for labelers
To sum up, video object detection is complex and requires appropriate tools to handle its intricacies. The process can be significantly streamlined by adopting best practices in data annotation and leveraging platforms such as Kili Technology's video annotation tool. This approach ensures that the resulting algorithms are practical, efficient, and ready to be deployed in real-world scenarios where accuracy is paramount.
Don't hesitate to try out Kili's video annotation tool now and experience the convenience of easily annotating videos!


When it is about identifying the images, humans can easily recognize and differentiate between various features and objects. It is because the brain of human beings continuously and unconsciously frames the images, and they have the capacity to differentiate things effortlessly. It is challenging for people to realize when they are interrupting in the real world. Finding different things of the visual world and then differentiating effortlessly is not a challenge for humans. At the same time, our subconscious mind carries the process without any difficulties.
On Contra, the human views require carrying numerical values and patterns to distinguish the digital image videos. The critical feature of recognizing the image is entirely different from human beings. For instance, computer vision can identify the pedestrians and the vehicles moving on the road with the help of face recognition and image recognization. These images are categorized into various categories and through which the computer vision can filter the uploaded image.
How Does The Image Recognization Property Work?
In this digital era, everything can be possible where the digital image helps in representing the numerical value did. These values help in representing the associated data with the support of pixel image. It is very intense as there are different pixels, and on average, every single value helps in representing itself from the matrix format.
All the information available helps the image recognition system to identify the location of the image. Through the information, the developed system can identify the image and find the relationship and pattern and tell the image source. All this is part of the learning process, and after the completion of the entire process, the system can perform the test data and the validation.
Therefore, there is no doubt that in today’s time; the increasing demand for autonomous vehicles is developing. Every year millions of money is spent on this industry because of which the demand for computer vision is stimulating. You can easily find more on my site and can gather information related to computer vision.


Dior has unveiled its latest eyewear creation to celebrate its long lasting partnership with lens manufacturer Carl Zeiss Vision. In addition to taking on the title of Official Eyewear Provider and Spectacle Lens Partner of Dior, Carl Zeiss Vision is also responsible for manufacturing bespoke lenses for Christian Dior's (Paris:CDI.PA +0.28%) sunglasses. The two brands have worked together since 2017, and their first collaboration included the limited edition CD Link collection released in September 2018.
The new designs are a follow-up to the CD Link line and continue to celebrate Dior's creative director Kim Jones' signature aesthetic of bold colors, contrasting patterns and unparalleled detailing. The new CD Link collection contains eight styles available in a variety of bold colour hues, from yellow and blue to red, while continuing to feature the CD Link logo subtly imprinted on the temples.
Various materials have been used for these sunglasses pieces including nylon, acetate and metal for frames and Carl Zeiss Vision lenses that are also used in Dior's eyewear collections. The selection of lenses allows for a wide variety of optical effects and perform well in different lighting conditions, ranging from UV-protected to polarized sunglasses built for both men and women.
These limited edition frames are set to be released later this year with prices starting at $300 USD (£239 GBP). A portion of the proceeds will also be donated to the Association Nationale de Prévention du Suicide (ANPS) in France - an organisation which aims to prevent suicide through awareness, programmes and research.
With each new collection, Kim Jones collaborates with different brands under Christian Dior's umbrella such as Christal Van Marc (Paris:COU) for the crystal-embellished jewelery and leather accessories.


By Gabriela Baron
The Department of Transportation (DOTr) announced on Tuesday that it will be installing 20 reels of fiber optic cables for the Metro Rail Transit System Line 3 (MRT-3).
The cables will be used for signaling and communications systems.
“Kung dati, copper wires lamang ang ginagamit para sa signaling system ng MRT-3, ngayon dahil sa kumprehensibong rehabilitasyon ng rail line, mapapalitan na ng mas modernong kagamitan ang system,” DOTr said in its official Facebook page.
(Before, we just use copper wires in MRT-3 signaling system, but because of the comprehensive rehabilitation of the rail line, we are now replacing them with more modern equipment.)
Sumitomo-MHI-TESP, the new maintenance provider of MRT-3, targets to complete the cable installation this year.
The DOTr and MRT-3 managements are currently testing and commissioning the fiber optics to ensure the quality of the wiring.
The railway signaling systems aim to keep the trains safe by keeping them at a safe distance from each other.


Laser has important applications in biology and medicine. In 1981, the World Health Organization listed laser medicine as a discipline of medicine. Laser technology is widely used from clinical diagnosis and treatment to basic medical research.
The application of laser in diagnosis is laser analysis instrument and laser tumor diagnostic analyzer, laser CT, laser holographic microscope and other laser diagnostic instruments; the application in treatment is laser knife, laser therapy machine, laser microbeam technology, endoscopic laser , Photodynamic therapy, etc. Laser can be used to treat eye diseases, anesthesia and blood tests, treat heart diseases and embolism of intravascular plaque, weld surgical wounds, treat tumors, etc.
Laser medicine is widely used and has special biological effects. It will play an increasingly important role in the field of biomedicine. Laser technology has penetrated into various fields of life sciences and has become a powerful weapon for solving clinical treatment problems.


The OTDR functionality underpinning this capability is based on the principle of measuring the time delay between transmission of a pulse of light from the optical transceiver and reception back at the transceiver of a corresponding pulse 'reflected' back from the discontinuity (i.e. the breakage or disconnection point). This is represented graphically in the diagram below.
Metrodata Ltd. is the strong first EDD vendor to incorporate full commercial support for automated OTDR-based fault detection in this manner, and is pleased to be working in co-operation with Optical Zonu Corp. to bring not only comprehensive Ethernet service monitoring in terms of throughput, packet-loss, latency and jitter, but now also physical layer fibre fault detection and localisation, to its Telecoms Carrier and Ethernet Service Provider customers.
Networking The World Since 1989


This is kind-of the same as ratchet freak's answer, but fleshed out a little more.
The usual way to handle this is with a special projector light type. It's like a spotlight, but (usually) with a square instead of circular fall-off. That is, when you cast a shadow ray to the light, you don't just measure a single angle (or radius) from the light's axis, you measure $(x, y)$ co-ordinates in the light's space. You divide these co-ordinates by the size of the projection (a parameter of the light just like the cone spotlight's angle parameter), and use the result for a texture look-up in the image you want to project.
If so, wouldn't any light ray bounce on that screen creating a mess?
If you're using a GI system, the high-frequency lighting detail that you get from a projector light will create a more challenging GI environment. You can control the badness by making the albedo of the surface you're projecting onto as small as you can get away with. If you don't need accurate GI results, you can use a proxy in the GI system instead of the real projector map. The proxy could have an aggressively blurred image, or even a normal spot-light whose colour is the average of the whole image. The blur technique is very applicable to a path tracer, while a single colour would be simplest for a radiosity or virtual lights implementation.


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|<![if !vml]><![endif]>||Optec produces a wide range of SWIR, V-SWIR, X-RAY and VIS Lenses as well as multichannel cameras, LASER systems and SPACE qualified lens systems . The Optec SWIR lineup covers all the range of focal lengths, from the smallest 16mm wide-angles, to super-telephotos focal length of 1000mm. Optec is able to provide custom bayonets mounts upon customer request. Customer can select the version that best suits his needs on Optec’s Catalog or inquire for a special customized version that will meet their exact requirements.|
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Last modified: March 19, 2014


The most versatile pro-grade flashlight in the world, with magnet clip.
The 3410M (magnet version) is the ultimate compact work light. Molded from “glow in the dark” photoluminescent material, this light is easy to find even when it’s off. The 3410M is equipped with both a spot and flood LED for close-up as well as distance, so you can have wide area light in the immediate work area and still penetrate darkness hundreds of yards away. It can be activated to run one or both LEDs with 3 levels of brightness. The articulating head and built-in clip allows the light to be directed wherever it is needed, while a heavy duty magnet allows the light to be snapped to steel surfaces.
The Peli 3410M- a professional grade multi-task lighting tool that will help you get the job done- under any circumstances.
"Glow in the Dark" Body
3 modes: Spot, Flood or Both
Articulating Light Head
Attached clip with integrated magnet
3 Light Levels (High, Medium, Low)
|BODY:||Photo luminescent Polycarbonate (PC)|
|LENS:||Clear Polycarbonate (PC)|
|SHROUD:||Photo luminescent Polycarbonate (PC)|
|CONTACT SPRING RETAINER:||Stainless Steel / Phosphore Bronze / Brass Nickel Plate|
|CLIP:||Photo luminescent Polycarbonate (PC)|
|LENGTH:||7.44" (18.9 cm)|
|WEIGHT WITH BATTERIES:||9.9 oz (281 gr)|
|WEIGHT WITHOUT BATTERIES:||7.4 oz (210 gr)|
|LAMP SECONDARY TYPE:||LED|
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|CONFIGURATIONS:||3410M - Standard|
3410M Right Angle Light
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The SharpStar 15028HNT is our latest product and development of super-fast focal ratio, hyperboloid and Newton reflecting telescope, which makes up the domestic gap in this field. Hyperboloid is much more complicated than traditional paraboloid, no matter in development, manufacture or detecting requirements. When compared to other similar products on the market, The SharpStar 15028HNT is in the leading position. Carbon fiber was used to create the lens barrel material, which is beautiful, durable and lightweight. It also provides best thermal stability by using Pyres glass.
The SharpStar 15028HNT provides a very good reflectivity (over 96%) thanks to the enhanced- Aluminium coating. The secondary cross is one-time molded, to ensure firm and hard to deform. The use of a precision 2.5" R.P. focuser offers larger payload and a stronger hold, so that your focuser will not slip when using heavy-duty observation accessories or camera.
The SharpStar 15028HNT is equipped with all CNC aluminum parts and a novelty handle, which enables the lifting of its mechanical quality and appearance to a new level, so that the overall product can more closely meet the needs of astronomers.
The SharpStar 15028HNT features a three-piece image corrector that helps to achieve full-frame photography and excellent perimeter dimming control. The focal plane is 55 mm, plus an M48X0.75 thread at the end of the focuser, the enormous back focal distance provides ample space for any visual or photographic equipment.
NGC2264 taken by Sharpstar 150 F2.8 Astrograph


The LCD projector is the modern day overhead projector. It is also used for displaying images. The LCD stands for liquid crystal display. It was invented in 1968 by a New Yorker, Gene Dolgoff. He was in college when he started the research. He was looking for a way to brighten the 3-CRT projectors that they had to use in those days. Then, he came up with the idea of depixelization and used liquid crystals to modulate the light. Since 2005 they have added the dynamic iris. Some of the fans think that this has improved contrast levels and now can compare them to the levels of the DLP.
An LCD projector does project light through a filter in order to create an image, just like other projectors. The early ones actually were used with the existing overhead projectors. The difference is that The LCD filter is malleable and can also be used to show videos and many more slides than a regular slide projector. There are three small LCD screens inside the projector. It forms three sides of the square, the forth side is open to the light. Red, blue and green are the colors of the screens. They beam through the screens and using mirrors, create an image.
Some people call all modern projectors a LCD projector. Actually, there are two types, both digital. The first one is called the DLP or the Digital Light Processing and the second type is called LCD or The Liquid Crystal Display projector. The LCD’s are thought to have great color saturation with the brightness and contrast adjustable. Of course, the higher the resolution of the projector, the higher the cost.


What You Should Know About Light And Your Artwork
UV Protection For Your Artwork
All this talk about invisible UV rays would seem to spell certain doom for artwork, but thankfully that is not the case. A development in glass technology by Tru Vue, Inc. has brought about a glass product known as Conservation Series which effectively blocks out 97% of all harmful UV rays. The cost premium for using conservation glass is marginal, and the benefits of its use cannot be ignored.
The single most important step you can take to prevent harmful UV rays is to specify the use of conservation glass by Tru Vue.
Tru Vue conservation glass is available in clear and reflection control. Depending on the intensity of the light in the room where your art will be displayed, you may want to request Conservation Reflection Control. Reflection Control glass deflects "hot spots" and glare, so the beauty of the art can show through.
This Tip Courtesy of
� 1999 Tru Vue, Inc.
Check Back For More Tips!


Smart Light with ARTIK Cloud
Here we provide technical details of using an ARTIK 055s module to implement a smart dimmable light, based on the Tizen RT RTOS. The smart light application can be used with the Samsung ARTIK mobile app to turn on, turn off, and adjust brightness of the light. A physical on-device button is also provided for a more traditional way of controlling light.
By using the Samsung ARTIK mobile app, users can easily on-board the smart light to ARTIK Cloud. Over-the-air (OTA) firmware update capability is included in the smart light application as well.
There are 5 pre-set brightness levels that can be used to control the LED brightness.
|Control Action||Brightness Level (%)|
The above 5 brightness settings can be used both locally by pressing physical button (SW703 on starter kit), and remotely by selecting the particular action using mobile app through ARTIK Cloud.
There is an additional remote action, SetBrightness, that can be supported by using the mobile app. It can be used to remotely adjust LED brightness to any preferable brightness level between 0% to 100%.
Once the ARTIK 055s smart light device is powered up and connected to Wi-Fi and ARTIK Cloud through the built-in onboarding process, the smart LED light is ready to use.
The smart light Tizen RT application can be used as a reference to develop the connected dimmable LED lamp project for the ARTIK 05x module.
ARTIK 055s dev board
LED (as light)
Resistor (100 Ω)
A universal micro-USB cable to interface with your development machine
A Linux cross-compilation development environment is required to develop applications using Tizen RT software. For more information about how to setting up Tizen RT development environment, refer to the advanced developers article. Here we use VirtualBox running on a Windows 7 PC.
- The source code can be download from: https://github.com/SamsungARTIK/AppNotes-SmartLight
Connect External Circuit
Before you can run the smart light application to experience of remotely control dimmable LED light, you will need to connect to external circuit. Connect the circuit as instructed in the picture below (or refer to the GitHub hardware article ) The ARTIK 055s starter kit pinout map can be found here.
The smart light application pre-programs the connection to the “XPWMTOUT_3” pin. We are using pulse width modulation (PWM) to drive the LED in this application, meaning that the LED is getting switched on and off. When the switching frequency is high enough, the brightness of the LED is integrated over time, and human eyes cannot tell the difference. We are setting the switching frequency of the LED to be 1000Hz in the code, turning the LED on for 0.1 millisecond and then off for another 0.9 millisecond.
When powering an LED, it’s always a good practice to connect a resistor to limit the current though the LED and prevent its burnout. The LED cathode (-) pin is the pin right next to the flat spot of the LED, and connects to ground. The anode (+) is the other pin that connects to the resistor and XPWMTOUT_3.
When Tizen RT development environment is set up, you can download or clone the smart light application from the GitHub repo. Follow the instructions to build the smart light Tizen RT application. When the application builds successfully, you will see library size information output at the console as shown.
Program Tizen RT binary
After successfully building Tizen RT application, follow the instructions to flash the image to the target ARTIK 055s device.
You will see something like shown in the picture below. Each of the flash partitions is loaded to the target board. For more information about ARTIK 055s flash partitions refer to the memory map.
Register and log in to your ARTIK Cloud account.
When creating a new device type under your ARTIK Cloud, you can just load the pre-defined manifest.json file for smart light device found here.
After creating the device, the other thing you need to do is to browse to “Device Management” tab under your newly created device, then click “SET UP PROPERTIES” to enable server properties and device properties and allow LWM2M to run for your device.
Click here for more information about device management and LWM2M.
Now we are ready to run the smart light application. Connect the micro USB cable from ARTIK 055s to host PC. Start the terminal emulator program as instructed here. After pressing the reset button (SW700 located on the ARTIK 055s starter board), you should see the TASH shell CLI interface like shown in the screen capture below:
Note that the commit hash under system information reflects a commit ID of the source repository. So the commit hash may have been changed by the time you build your smart light application. This is totally fine.
At the TASH shell prompt, type “help”. All of the currently available applications will be displayed. “smartlight” is one of them.
The “smartlight” application will start.
Now, refer to the getting started tutorial to:
Download mobile app
Provision Wi-Fi to your smart light device
Onboard smart light device onto ARTIK Cloud
|After successfully onboarding the smart light, from your mobile app, you should be able to see the smart light device appears under your ARTIK Cloud account.|
|If you click on the smart light device you just added, all of the actions defined for the device manifest file will be shown. You can click on the action to change the state of the LED light remotely.|
|All of the actions, except SetBrightness do not require additional setup. As mentioned in previous section, the brightness level can be set between 0% and 100%. To set the preferred brightness level, click on SetBrightness action from the mobile app device action list, then type in integer between 0 and 100 in the text field.|
Also note that the brightness of the LED light can also be changed anytime by pressing SW703.
When SW703 is pressed, the terminal will also show messages to indicate current LED brightness level.
Button press once would turn “ON” the LED light.
Button press twice would change the LED light to “READING” mode
Button press three times would change the LED light to “MOVIE” mode
Button press four times would change the LED light to “SLEEP” mode
Button press five times would change the LED light to “OFF” mode
Generate OTA Image
There are cases that you may want to update your application firmware to add more functionality or to fix bugs. The smart light application also has OTA capabilities built in to the code base. When you finish updating your firmware, make sure that you’ve also updated the firmware version. The firmware version is defined by the following line in the smart light header code
#define ONBOARDING_VERSION "126.96.36.199"
After that, build the application as mentioned in earlier section. Then, run the command below to generate a special binary for OTA:
make image ota
Note that the resulting "ota.bin" can be found under /build/configs/$(BOARD_NAME)/bin
This ota.bin image can be uploaded into ARTIK Cloud device management for OTA update. When uploading new OTA update image, you need to choose “Edge Node” as the type of target device. Make sure you enter the same firmware version number as you programmed in the code.
Check the OTA article for more details about OTA.


Preview on Haul Truck
We’ve Got Your Blind Spot Covered
The in-cab display provides the driver with an audible and visual alert when an object is in the detection zone. As the object moves closer to the sensor, the audible alert increases and more LEDs light up on the display.
An Xtreme PreView® sensor is mounted on the front, rear, right side and left side. This covers the most dangerous blind spots.
Replace in-cab LED display with PreView Plus camera and monitor system. Superimposes object detection indicators directly on to monitor and will support up to 4 cameras.
- Reduce Blind Spot Accidents
- Actively Detect Objects


SYSTEMS, ISSUES, AND PROCEDURES
1. The CCD Camera and Its Attachment to the Telescope
The number and quality of CCD cameras available to the amateur astronomer grows daily. From the person willing to spend thousands of dollars to the person with only a few hundred dollars and a willingness to build a camera, there are CCD systems to match both the pocketbook and imaging needs.
CCD cameras have inherent characteristics that should be briefly noted. Each CCD chip and its attendant electronics have varying degrees of noise. The less noise the better the CCD will be at imaging faint objects. The most prevalent sources of noise are readout noise and dark current (thermal) noise. (See the discussion of CCD signal and noise in Section 4.f. Achieving Good SNR)
Dark current refers to the build up of signal on the CCD chip -- even when no light is present -- due to the simple presence of heat. The noise component of dark current is significant relative to the faintness of most astronomical objects. The amount of dark current drops off quickly with lower temperatures. By cooling a CCD chip, the dark current can be reduced to an acceptable level and its effects can further be mitigated in the calibration process. Good astronomical CCDs have cooling systems that can lower chip temperatures by 35 degrees C or more.
Readout noise exists because CCD camera electronics cannot determine and portray with perfect accuracy how many electron volts are stored in each pixel from impinging photons. There is sampling error which introduces noise into the system. Some cameras have "double correlated sampling" which in effect samples each pixel twice to measure as accurately as possible how many electron volts are resident in each pixel. Once the analog voltage from each pixel is determined it can be digitized, but low-level random variations in the digital output further contribute to readout noise.
The analog voltage in each pixel is digitized through the camera's analog-to-digital (A/D) conversion electronics. CCD cameras convert the analog signal into digital increments so that the image can be represented by pixel grayscale values. The number of increments your CCD divides the analog signal into determines the precision of its representation of the imaged object. Good cameras have at least 12-bit A/D conversion (4096 levels) with most having 16-bit A/D conversion (65535 levels).
Many thousands of words could be inserted here on system trade-off parameters: cost, readout noise, quantum efficiency across the imaging spectrum, system gain and dynamic range, system cooling method and efficiency, shuttering, etc.; but once the imager has decided on a camera and is ready to put it to use, these parameters are locked in and only a few items are continually important to take into consideration when planning an imaging session. These are:
a. Pixel size and chip size. When taken into account with the focal length of the optical system, these measurements are key to establishing the FOV that will be imaged and the resolution (i.e., level of fine detail) that the image will provide. As stated in the Glossary, the approximate width of the FOV in arcseconds can be calculated by dividing the width of the CCD chip in microns by the focal length of the optical system in millimeters and multiplying the result by 206. Similarly the FOV, or angular resolution, of each pixel can be determined using the same equation, substituting the width of a single pixel for the width of the entire CCD chip. To achieve images wherein no information is lost due to insufficient resolution of the data available from the optical system, each pixel should be no larger than 50% of the size of the expected image point spread function (PSF -- see the Glossary). The imager should try to match focal length to pixel size and chip size to plan for the appropriate system resolution and appropriate FOV for the object(s) being imaged. Sky seeing conditions and other factors will often mitigate the imager's best plans, but establishing appropriate imaging system parameters for your camera will lead to best long-term results. See http://www.nto.org/whatusee_en.html for a Windows-based program to help calculate the FOV for an imaging system.
b. Chip location at the focal plane. When first using a camera, the imager may find that reaching focus is difficult due to uncertainty about the location of the optical system's focal plane or the recessed location of the chip in the camera. The chip must be positioned at the focal plane to bring the camera into focus. One easy way to determine the location of the focal plane is to mark the location of an eyepiece's focal plane (same as the field stop) on the outside of the eyepiece and then bring an eyepiece into focus on a star. The mark on the outside of the eyepiece will then match the position of the telescope's focal plane. Coupling this knowledge with knowledge of the distance that the chip is recessed inside the camera housing should allow the imager to determine whether the CCD system can be brought into focus.
c. Vignetting. It is very important to minimize vignetting of the CCD chip. In order for the all portions of the chip to be illuminated by the full diameter of the telescope's primary optic, the secondary mirror and other optical elements must be of sufficient size and adequate design, and any apertures created by the focuser mechanism or system baffles must be wide enough to not restrict the optical path. Any vignetting will result in noticeable degradation of signal values in the outer regions of the chip. The darkening effect on sky background signal in peripheral regions can be reversed by flat-fielding, but lost SNR cannot be restored. Perhaps the easiest way to inspect for potential vignetting is to cut an aperture in a piece of paper the size and shape of the chip, then place this aperture at the focal plane during the daytime and look through it with the eye directly behind the aperture. Look at the primary optic to see if its entire surface can be seen through all portions of the aperture. If it can there should be no vignetting; but if it is partly obscured, then it should be readily apparent which part of the interceding optical or mechanical structure needs to be changed. See http://home.att.net/~dale.keller/atm/newtonians/newtsoft/newtsoft.htm for a Windows-based program to help determine vignetting of a Newtonian optical system.
d. Using a turret or slide or flip-mirror to center objects visually. One of the more difficult imaging tasks can be locating and accurately centering an object in the CCD's FOV. Unless you are fortunate enough to have a perfectly aligned system with extremely accurate automatic pointing, objects must be located within the FOV by manual means (i.e., star hopping and use of low-power finders). Even amateurs accustomed to manual visual work will be challenged and often frustrated by this requirement unless some means is adopted for centering objects visually within the main scope's eyepiece FOV before replacing the eyepiece with the CCD camera. This is where a two-position turret or slide mechanism or a flip-mirror assembly to divert the optical path to a fixed eyepiece comes in handy. Having to exchange an eyepiece and a camera in the same focuser tube requires constant rechecking of focus and creates additional flat-fielding requirements since camera orientation is unavoidably changed. Using a two-position rotating turret or slide assembly or a flip-mirror assembly that has focuser tubes for both an eyepiece and the CCD camera solves the problem by allowing visual centering before moving the fixed-orientation CCD into the eye's position. In addition, the finding/centering eyepiece can be parfocalized with the CCD chip so that changes in telescope focus can be accommodated rapidly by visual monitoring and adjustment as the night progresses. Please go to http://www.ghg.net/cshaw/slide.htm to see more about turret and slide designs. See http://members.aol.com/STRG8ZR/homeyer/homeyer.html for Andy Homeyer's flip mirror.
e. Filter wheels/slides. If you plan to do filtered imaging to concentrate on specific wavelengths or produce color images, attaching a rotating or sliding filter-holder mechanism in front of the camera provides a great advantage. Without such a device, filters must be separately and manually inserted into the optical path, requiring careful refocusing and unavoidably changing the camera's flat-field orientation. Filters carefully selected to have the same thickness will be parfocal in a filter wheel or slide, allowing quick filter changes without refocusing. Please see http://www.ghg.net/cshaw/filter.htm to see Andy Saulietis' filter wheel design. See http://members.aol.com/STRG8ZR/homeyer/homeyer.html for Andy Homeyer's filter wheel and other accessories.


Dynamic light scattering, also known as quasi elastic light scattering (Quels) and photon correlation spectroscopy (PCS), measures the laser light that is scattered from dissolved macromolecules or suspended particles. Due to the Brownian motion of the molecules and particles in solution fluctuations of the scattering intensity can be observed.
According to the semiclassical light scattering theory when light impinges on matter, the electric field of the light induces an oscillating polarization of electrons in the molecules. The molecules then serve as secondary source of light and subsequently radiate (scatter) light. The frequency shifts, the angular distribution, the polarization, and the intensity of the scatter light are determined by the size, shape and molecular interactions in the scattering material.
DLS measures the scattered light of a sample in very short time periods and correlates the data. Due to the fact that large molecules or particles move slower than small molecules a defined correlation function results. From the correlation function the diffusion coefficient (D) of the molecules can be calculated by fitting the data.By fitting the points of autocorrelation to the function G(t), the diffusion coefficient can be measured and related to the equivalent sphere of diameter d using the Stokes – Einstein equation:
n = diluent viscosity (water = 8.94*10-4 kg/(ms)
T = Temperature (K) (room temp = 298 K)
D = diffusion coefficient (in m2/s)
kB = Boltzmann constant (1.3807*10-23 J/K)
d = sphere diameter (m)
The DLS technique is especially impressive because there is no sample information needed. Only the solvent viscosity and the refractive index of the solvent must be known. For nanoparticles the density of the particles might be important. The refractive index increment, an important parameter in static light scattering experiments, is not involved in the calculation of Rh.


We will realize safety measures with excellent cost performance as we aim to create safe and comfortable platforms.
This sophisticated surveillance system uses a compact outdoor 3D laser ranging image sensor developed in-house to detect passengers approaching the edge of the platform. When the sensor detects danger, it issues a warning and prevents accidents such as accidental contact with a train car or falling.
Features of Platform Sensing System
Sensors that are resistant to sunlight are installed on the top or side of the platform to ensure the safety of the platform.
When detecting dangers such as accidental contact, passengers standing at a platform edge and accidental falls that are likely to occur every day, the system issues warnings and alarms with built-in speakers and LED line lights. It also operates simultaneously with an emergency stop device and reports to station staff.
The sensors can be installed facing down or sideways according to the station environment.
3D Laser Ranging Image Sensor
Our proprietary Infini Soleil 3D laser ranging image sensor is a sensor that is resistant to ambient light, perfectly designed for outdoor-use, and can recognize 3D shapes.
The sensor system uses laser light to detect people and objects within an area.
Employing an active system with an internal near-infrared pulse laser, it can be used at all hours of the day and night. With a unique optical structure developed by Nippon Signal, Infini Soleil exhibits excellent robustness in sensing obstacles and processing noise, making it possible to reliably detect obstacles even in diverse outdoor environments.
- Reliably recognizes obstacles based on 3D information.
- Employs a long-life MEMS mirror (ECOSCAN) as an optical scanner.
- Resistant to ambient light (sunlight) and can be used at all hours of the day and night.
- Unlike a camera, it does not capture an image of the actual object, and therefore poses no privacy issues.
- “Class 1” laser safety standard (causing no harm to human eyes).
LED Line Light
Shines a warning light when detecting a person standing at a platform edge.
When a passenger approaches the warning line at the edge of the platform, the high-brightness LED line light emits light. The warning line emits light to give a warning that tells the passenger not to go any further.


This method can detect a blank space in an image consisting of some frames and then segments the image into each frame surrounded by a blank space. Since the segmentation is done in a hierarchical way,the sequence can be inferred in which each frame is looked at.The method can also segment an image into frames, even if a frame is not surrounded by a straight line.
The near-infrared spectroscopy can immediately analyze material composition. To simply measure a body fat percentage of small animals, non-invasive method by using this near-infrared spectroscopy has been established.
The usage of traditional molecular sieves are limited, because an exact control of the pore size is very difficult. On the other hand, the pore entrance size of a mesoporous silica covered with temperature-responsive polymer can be precisely controlled by changing the water temperature.


Vision-based detection and tracking algorithms have evolved significantly over the past ten years. Where features used to be engineered, they are now automatically determined with deep learning. Where tracking algorithms worked well with a few objects, they now perform well with many objects.
Automated camera-based inspection algorithms are often used in the mining and manufacturing industry to detect failures, quality defects and hazardous conditions. In this 30-minute webinar we will present a practical example of an automated inspection algorithm.
What Can I Do Next?
- Watch the webinar
- Request a trial.
- Find out more from the team.
- Visit our Smart Mining & Manufacturing Focus Area page to learn more.


The above demo shows 1/2 of the ingredients of a "true" multi-touch display.
To make images appear under your fingers instead of over on the computer's screen, takes only a few more steps:
1) Remove the IR filter from the camera so it can detect infrared, then add a filter over the lens to ignore visible light, so the camera only "sees" IR.
2) Add an IR source inside the box, pointing up (many commercially available security cameras already come configured with IR "blast" arrays, or they can be purchased as a separate unit, or make your own).
3) Exchange the tracing paper for a material that is still translucent yet allows IR light thru.
4) Add a little LCD projector next to the camera in the box, also pointed up.
Now your hands will be bathed in invisible infrared light. As your fingers approach and/or touch the surface, the camera "sees" IR blobs, which are then processed in software to differentiate touch and other gestures. The "Touchlib" library includes everything you need to process the images (as seen in the video). Now you project the image up onto the touch surface from below. The image is projected in the visible spectrum and so doesn't interfere with the IR blobs being received by the camera.
There is also a way to get crisper IR blobs, without bathing the user's hands in IR, by using a technology called "Frustrated Total Internal Refraction" (FTIR), popularized by Jeff Han. FTIR relies on the unique properties of acrylic sheet, e.g. it is extremely reflective on the inside surface, until touched (or until it gets dirty). By shooting IR into the edges of the sheet, touches are "seen" by the IR camera whenever the internal refractive properties of the acrylic are "frustrated" by being touched. Each touch bounces a blob of IR down into the camera.
I've been having some great conversations about multi-touch lately. What constitutes multi-touch? Does dual-touch count? Can multi-touch be "faked" on a single-touch surface using close timing of events (e.g. milliseconds apart)? What if you use the same approach on a dual-touch surface? Is this how the iPhone works (e.g. software detection of finger-spacing)? What about the Macbook's dual-touch mousepad? How does that work, anyways? Which technologies are dominating, if any? Capacitive, grid o' wires, heat sensing, IR bath? Which approach is typically used for which commercial application?