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Context-aware reinforcement learning for re-identification in a video network

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2 Author(s)
Thakoor, N. ; Center for Res. in Intell. Syst., Univ. of California, Riverside, Riverside, CA, USA ; Bhanu, B.

Re-identification of people in a large camera network has gained popularity in recent years. The problem still remains challenging due to variations across cameras. A variety of techniques which concentrate on either features or matching have been proposed. Similar to majority of computer vision approaches, these techniques use fixed features and/or parameters. As the operating conditions of a vision system change, its performance deteriorates as fixed features and/or parameters are no longer suited for the new conditions. We propose to use context-aware reinforcement learning to handle this challenge. We capture the changing operating conditions through context and learn mapping between context and feature weights to improve the re-identification accuracy. The results are shown using videos from a camera network that consists of eight cameras.

Published in:

Distributed Smart Cameras (ICDSC), 2013 Seventh International Conference on

Date of Conference:

Oct. 29 2013-Nov. 1 2013