Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FANTrack: 3D Multi-Object Tracking with Feature Association Network   We propose a data-driven approach to online multi-object tracking (MOT) that
uses a convolutional neural network (CNN) for data association in a
tracking-by-detection framework. The problem of multi-target tracking aims to
assign noisy detections to a-priori unknown and time-varying number of tracked
objects across a sequence of frames. A majority of the existing solutions focus
on either tediously designing cost functions or formulating the task of data
association as a complex optimization problem that can be solved effectively.
Instead, we exploit the power of deep learning to formulate the data
association problem as inference in a CNN. To this end, we propose to learn a
similarity function that combines cues from both image and spatial features of
objects. Our solution learns to perform global assignments in 3D purely from
data, handles noisy detections and a varying number of targets, and is easy to
train. We evaluate our approach on the challenging KITTI dataset and show
competitive results. Our code is available at
https://git.uwaterloo.ca/wise-lab/fantrack.

31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn