Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Spatially Supervised Recurrent Convolutional Neural Networks for Visual
  Object Tracking   In this paper, we develop a new approach of spatially supervised recurrent
convolutional neural networks for visual object tracking. Our recurrent
convolutional network exploits the history of locations as well as the
distinctive visual features learned by the deep neural networks. Inspired by
recent bounding box regression methods for object detection, we study the
regression capability of Long Short-Term Memory (LSTM) in the temporal domain,
and propose to concatenate high-level visual features produced by convolutional
networks with region information. In contrast to existing deep learning based
trackers that use binary classification for region candidates, we use
regression for direct prediction of the tracking locations both at the
convolutional layer and at the recurrent unit. Our extensive experimental
results and performance comparison with state-of-the-art tracking methods on
challenging benchmark video tracking datasets shows that our tracker is more
accurate and robust while maintaining low computational cost. For most test
video sequences, our method achieves the best tracking performance, often
outperforms the second best by a large margin.

8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys