Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICDSC.2013.6778207 | 2013 Seventh International Conference on Distributed Smart Cameras (ICDSC) |
Keywords | DocType | Citations |
context-aware reinforcement learning,video network,camera network,computer vision,vision system,feature weights,reidentification accuracy | Conference | 1 |
PageRank | References | Authors |
0.36 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ninad Thakoor | 1 | 94 | 13.39 |
| Bir Bhanu | 2 | 3356 | 380.19 |