{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/intelligent-camera-selection-decisions-for","title":"Intelligent Camera Selection Decisions for Target Tracking in a Camera Network","arxiv_id":null,"date":"2022-01-08","proceeding":"IEEE WACV 2022 1","authors":["Anil Sharma","Saket Anand","Sanjit K. Kaul"],"abstract":"Camera Selection Decisions (CSD) are highly useful for several applications in a multi-camera network. For example, CSD benefit multi-camera target tracking by reducing the number of candidate cameras to look for the target’s next location. The correct candidate cameras, decreases the number of false Re-ID queries as well as the computation time. Also, in multi-camera trajectory forecasting (MCTF) to predict where a person will re-appear in the camera network along with the transition time. These applications require a large amount of annotated data for training. In this paper, we use state-representation learning with a reinforcement learning based policy to effectively and efficiently make camera selection decisions. We further demonstrate that by using learned state representations, as opposed to hand-crafted state variables, we are able to achieve stateof-the-art results on camera selection, while reducing the training time for the RL policy. Along with this, we use a reward function that helps to reduce the amount of supervision in training the policy in a semi-supervised way. We report our results on four datasets: NLPR MCT, DukeMTMC, CityFlow, and WNMF dataset. We show that an RL policy reduces unnecessary Re-ID queries and therefore the false alarms, scales well to larger camera networks, and is target-agnostic.","url_abs":"https://openaccess.thecvf.com/content/WACV2022/html/Sharma_Intelligent_Camera_Selection_Decisions_for_Target_Tracking_in_a_Camera_WACV_2022_paper.html","url_pdf":"https://openaccess.thecvf.com/content/WACV2022/papers/Sharma_Intelligent_Camera_Selection_Decisions_for_Target_Tracking_in_a_Camera_WACV_2022_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"intelligent-camera-selection-decisions-for","repo_url":"https://github.com/anilsh/tracking_camsel","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"trajectory-forecasting","task_name":"Trajectory Forecasting"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}