{"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/cameltrack-context-aware-multi-cue-1","title":"CAMELTrack: Context-Aware Multi-cue ExpLoitation for Online Multi-Object Tracking","arxiv_id":"2505.01257","date":"2025-05-02","proceeding":null,"authors":["Vladimir Somers","Baptiste Standaert","Victor Joos","Alexandre Alahi","Christophe De Vleeschouwer"],"abstract":"Online multi-object tracking has been recently dominated by tracking-by-detection (TbD) methods, where recent advances rely on increasingly sophisticated heuristics for tracklet representation, feature fusion, and multi-stage matching. The key strength of TbD lies in its modular design, enabling the integration of specialized off-the-shelf models like motion predictors and re-identification. However, the extensive usage of human-crafted rules for temporal associations makes these methods inherently limited in their ability to capture the complex interplay between various tracking cues. In this work, we introduce CAMEL, a novel association module for Context-Aware Multi-Cue ExpLoitation, that learns resilient association strategies directly from data, breaking free from hand-crafted heuristics while maintaining TbD's valuable modularity. At its core, CAMEL employs two transformer-based modules and relies on a novel association-centric training scheme to effectively model the complex interactions between tracked targets and their various association cues. Unlike end-to-end detection-by-tracking approaches, our method remains lightweight and fast to train while being able to leverage external off-the-shelf models. Our proposed online tracking pipeline, CAMELTrack, achieves state-of-the-art performance on multiple tracking benchmarks. Our code is available at https://github.com/TrackingLaboratory/CAMELTrack.","url_abs":"https://arxiv.org/abs/2505.01257v1","url_pdf":"https://arxiv.org/pdf/2505.01257v1.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":"cameltrack-context-aware-multi-cue-1","repo_url":"https://github.com/TrackingLaboratory/CAMELTrack","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"multi-object-tracking","task_name":"Multi-Object Tracking"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"online-multi-object-tracking","task_name":"Online Multi-Object Tracking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-object-tracking-on-dancetrack","task":"Multi-Object Tracking","dataset":"DanceTrack","model":"CAMELTrack (fully online)","rank_in_archive_order":7,"of":37,"metrics":{"HOTA":"69.3"},"uses_additional_data":false},{"leaderboard":"/sota/multi-object-tracking-on-mot17","task":"Multi-Object Tracking","dataset":"MOT17","model":"CAMELTrack (fully online)","rank_in_archive_order":19,"of":48,"metrics":{"AssA":"61.4","DetA":"63.6","HOTA":"62.4","IDF1":"63.6","MOTA":"78.5"},"uses_additional_data":false},{"leaderboard":"/sota/multi-object-tracking-on-sportsmot","task":"Multi-Object Tracking","dataset":"SportsMOT","model":"CAMELTrack (fully online)","rank_in_archive_order":3,"of":22,"metrics":{"AssA":"72.8","DetA":"88.8","HOTA":"80.4","IDF1":"84.8","MOTA":"96.3"},"uses_additional_data":false},{"leaderboard":"/sota/online-multi-object-tracking-on-sportsmot","task":"Online Multi-Object Tracking","dataset":"SportsMOT","model":"CAMELTrack","rank_in_archive_order":1,"of":1,"metrics":{"HOTA":"80.4"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}