{"url":"/method/lmot","slug":"lmot","name":"LMOT","full_name":"LMOT: Efficient Light-Weight Detection and Tracking in Crowds","full_name_withheld":false,"description_markdown":"Rana Mostafa, Hoda Baraka and AbdelMoniem Bayoumi\r\n\r\n**LMOT**, i.e., Light-weight Multi-Object Tracker,  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.\r\n\r\nCode: https://github.com/RanaMostafaAbdElMohsen/LMOT\r\nPaper: https://doi.org/10.1109/ACCESS.2022.3197157","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":null,"title":null,"url_on_a_paper_host":false},"code_snippet_url":"https://github.com/RanaMostafaAbdElMohsen/LMOT","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Multi-Object Tracking Models","url":"/methods/category/multi-object-tracking-models","pwc_aliases":[]}],"n_papers_tagged":2,"archive_num_papers":2,"papers_newest_first":[{"paper":"/paper/multi-object-tracking-in-the-dark","title":"Multi-Object Tracking in the Dark","date":"2024-05-10","arxiv_id":"2405.06600","n_code_links":1,"syntology":null},{"paper":"/paper/lmot-efficient-light-weight-detection-and","title":"LMOT: Efficient Light-Weight Detection and Tracking in Crowds","date":"2022-08-08","arxiv_id":null,"n_code_links":1,"syntology":null}],"papers_shown":2,"tasks":[{"task":"/task/multi-object-tracking","name":"Multi-Object Tracking","papers":2},{"task":"/task/object","name":"Object","papers":2},{"task":"/task/object-tracking","name":"Object Tracking","papers":2},{"task":"/task/2d-object-detection","name":"2D Object Detection","papers":1},{"task":"/task/autonomous-driving","name":"Autonomous Driving","papers":1},{"task":"/task/pedestrian-detection","name":"Pedestrian Detection","papers":1}],"tasks_shown":6,"n_tasks":6,"usage_by_year":[{"year":"2022","papers":1},{"year":"2024","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/lmot"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}