{"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/features-for-multi-target-multi-camera","title":"Features for Multi-Target Multi-Camera Tracking and Re-Identification","arxiv_id":"1803.10859","date":"2018-03-28","proceeding":"CVPR 2018 6","authors":["Ergys Ristani","Carlo Tomasi"],"abstract":"Multi-Target Multi-Camera Tracking (MTMCT) tracks many people through video\ntaken from several cameras. Person Re-Identification (Re-ID) retrieves from a\ngallery images of people similar to a person query image. We learn good\nfeatures for both MTMCT and Re-ID with a convolutional neural network. Our\ncontributions include an adaptive weighted triplet loss for training and a new\ntechnique for hard-identity mining. Our method outperforms the state of the art\nboth on the DukeMTMC benchmarks for tracking, and on the Market-1501 and\nDukeMTMC-ReID benchmarks for Re-ID. We examine the correlation between good\nRe-ID and good MTMCT scores, and perform ablation studies to elucidate the\ncontributions of the main components of our system. Code is available.","url_abs":"http://arxiv.org/abs/1803.10859v1","url_pdf":"http://arxiv.org/pdf/1803.10859v1.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":[],"tasks":[{"task_slug":"person-re-identification","task_name":"Person Re-Identification"},{"task_slug":null,"task_name":"Triplet"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/person-re-identification-on-market-1501","task":"Person Re-Identification","dataset":"Market-1501","model":"ATWL [ristani2018features]","rank_in_archive_order":96,"of":135,"metrics":{"Rank-1":"89.4","mAP":"75.6"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.10859","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}