{"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/lmot-efficient-light-weight-detection-and","title":"LMOT: Efficient Light-Weight Detection and Tracking in Crowds","arxiv_id":null,"date":"2022-08-08","proceeding":"IEEE Access 2022 8","authors":["Rana Mostafa","Hoda Baraka","AbdElMoniem Bayoumi"],"abstract":"Multi-object tracking is a vital component in various robotics and computer vision applications. However, existing multi-object tracking techniques trade off computation runtime for tracking accuracy leading to challenges in deploying such pipelines in real-time applications. This paper introduces a novel real-time model, LMOT, i.e., Light-weight Multi-Object Tracker, that performs joint pedestrian detection and tracking. LMOT introduces a simplified DLA-34 encoder network to extract detection features for the current image that are computationally efficient. Furthermore, we generate efficient tracking features using a linear transformer for the prior image frame and its corresponding detection heatmap. After that, LMOT fuses both detection and tracking feature maps in a multi-layer scheme and performs a two-stage online data association relying on the Kalman filter to generate tracklets. We evaluated our model on the challenging real-world MOT16/17/20 datasets, showing LMOT significantly outperforms the state-of-the-art trackers concerning runtime while maintaining high robustness. LMOT is approximately ten times faster than state-of-the-art trackers while being only 3.8% behind in performance accuracy on average leading to a much computationally lighter model.","url_abs":"https://ieeexplore.ieee.org/document/9852199","url_pdf":"https://doi.org/10.1109/ACCESS.2022.3197157","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":"lmot-efficient-light-weight-detection-and","repo_url":"https://github.com/RanaMostafaAbdElMohsen/LMOT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"2d-object-detection","task_name":"2D Object Detection"},{"task_slug":"multi-object-tracking","task_name":"Multi-Object Tracking"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"pedestrian-detection","task_name":"Pedestrian Detection"}],"methods":[{"method_slug":"lmot","method_name":"LMOT"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-object-tracking-on-mot16","task":"Multi-Object Tracking","dataset":"MOT16","model":"LMOT","rank_in_archive_order":8,"of":24,"metrics":{"IDF1":"72.3","IDs":"669","MOTA":"73.2"},"uses_additional_data":true},{"leaderboard":"/sota/multi-object-tracking-on-mot17","task":"Multi-Object Tracking","dataset":"MOT17","model":"LMOT","rank_in_archive_order":33,"of":48,"metrics":{"IDF1":"70.3","MOTA":"72.0"},"uses_additional_data":false},{"leaderboard":"/sota/multi-object-tracking-on-mot20-1","task":"Multi-Object Tracking","dataset":"MOT20","model":"LMOT","rank_in_archive_order":25,"of":27,"metrics":{"IDF1":"61.1","MOTA":"59.1"},"uses_additional_data":true}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}