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Published Articles >> Table of Contents >> Abstract
17th International Conference on Pattern Recognition (ICPR'04) - Volume 2
pp. 748-751
Reinforcement Learning-Based Feature Learning for Object Tracking
Fang Liu, Shanghai Jiaotong University, China
Jianbo Su, Shanghai Jiaotong University, China
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2004.1334367
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| Abstract |
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Feature learning in object tracking is important because the choice of the features significantly affects system's performance. In this paper, a novel online feature learning approach based on reinforcement learning is proposed. Reinforcement learning has been extensively used as a generative model of sequential decision-making that interacts with uncertain environment. We extend this technique to feature selection for object tracking, and further add human-computer interaction to reinforcement learning to reduce the learning complexity and speed the convergence rate. Experiments of the object tracking are provided to verify the effectiveness of the proposed approach.
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 Additional Information
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Citation:
Fang Liu, Jianbo Su,
"Reinforcement Learning-Based Feature Learning for Object Tracking,"
icpr,
pp. 748-751,
17th International Conference on Pattern Recognition (ICPR'04) - Volume 2,
2004
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