Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Reinforcement Learning for Visual Object Tracking in Videos   In this paper we introduce a fully end-to-end approach for visual tracking in
videos that learns to predict the bounding box locations of a target object at
every frame. An important insight is that the tracking problem can be
considered as a sequential decision-making process and historical semantics
encode highly relevant information for future decisions. Based on this
intuition, we formulate our model as a recurrent convolutional neural network
agent that interacts with a video overtime, and our model can be trained with
reinforcement learning (RL) algorithms to learn good tracking policies that pay
attention to continuous, inter-frame correlation and maximize tracking
performance in the long run. The proposed tracking algorithm achieves
state-of-the-art performance in an existing tracking benchmark and operates at
frame-rates faster than real-time. To the best of our knowledge, our tracker is
the first neural-network tracker that combines convolutional and recurrent
networks with RL algorithms.

8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an