Name
Abstract | ||
|---|---|---|
Target tracking across a network of cameras has various applications in surveillance and forensics. These networks typically have cameras with non-overlapping fields of view, which necessitates target handovers involving approaches like target re-identification that are robust to illumination and pose variations of the target. Re-identification based target handovers are susceptible to false alarms, more so in a high data volume setting like a surveillance camera network. In this work, we learn to decide when to make a re-identification query and to which camera in the network. We model this camera selection problem as a sequential decision making problem and solve it using a Reinforcement Learning (RL) based policy, which selects one of the cameras for querying or decides to not query. Using a Deep Q-Network (DQN) type approach, we observed that existing experience replay (ER) methods with DQN are inadequate for learning an optimal policy when the actions are imbalanced. Employing standard ER techniques result in learning policies that are likely to be biased towards selecting a more frequent action and result in a poorer performance of the end task, in our case multi-camera tracking. To address this problem, we segregate the experiences from agent-environment interaction into multiple replay memories and sample independently from these to create a diverse minibatch. We demonstrate the performance of the proposed method on the NLPR MCT dataset and DukeMTMC dataset along with its computational benefits. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/BigMM50055.2020.00029 | 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM) |
Keywords | DocType | ISBN |
Camera Selection Decisions,Target Tracking,Camera Network,Multi-Camera Tracking,Re-identification Query,Stratified Experience Replay | Conference | 978-1-7281-9326-7 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anil Sharma | 1 | 1 | 2.09 |
| Mayank K. Pal | 2 | 0 | 0.34 |
| Saket Anand | 3 | 87 | 9.36 |
| Sanjit Krishnan Kaul | 4 | 567 | 39.70 |