{"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/simultaneous-detection-and-tracking-with","title":"Simultaneous Detection and Tracking with Motion Modelling for Multiple Object Tracking","arxiv_id":"2008.08826","date":"2020-08-20","proceeding":"ECCV 2020 8","authors":["Shi-Jie Sun","Naveed Akhtar","Xiang-Yu Song","HuanSheng Song","Ajmal Mian","Mubarak Shah"],"abstract":"Deep learning-based Multiple Object Tracking (MOT) currently relies on off-the-shelf detectors for tracking-by-detection.This results in deep models that are detector biased and evaluations that are detector influenced. To resolve this issue, we introduce Deep Motion Modeling Network (DMM-Net) that can estimate multiple objects' motion parameters to perform joint detection and association in an end-to-end manner. DMM-Net models object features over multiple frames and simultaneously infers object classes, visibility, and their motion parameters. These outputs are readily used to update the tracklets for efficient MOT. DMM-Net achieves PR-MOTA score of 12.80 @ 120+ fps for the popular UA-DETRAC challenge, which is better performance and orders of magnitude faster. We also contribute a synthetic large-scale public dataset Omni-MOT for vehicle tracking that provides precise ground-truth annotations to eliminate the detector influence in MOT evaluation. This 14M+ frames dataset is extendable with our public script (Code at Dataset <https://github.com/shijieS/OmniMOTDataset>, Dataset Recorder <https://github.com/shijieS/OMOTDRecorder>, Omni-MOT Source <https://github.com/shijieS/DMMN>). We demonstrate the suitability of Omni-MOT for deep learning with DMMNet and also make the source code of our network public.","url_abs":"https://arxiv.org/abs/2008.08826v1","url_pdf":"https://arxiv.org/pdf/2008.08826v1.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":"simultaneous-detection-and-tracking-with","repo_url":"https://github.com/shijieS/DMMN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"simultaneous-detection-and-tracking-with","repo_url":"https://github.com/shijieS/OMOTDRecorder","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"simultaneous-detection-and-tracking-with","repo_url":"https://github.com/shijieS/OmniMOTDataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"multiple-object-tracking","task_name":"Multiple Object Tracking"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-tracking","task_name":"Object Tracking"}],"methods":[],"datasets_introduced":[{"slug":"omni-mot","name":"Omni-MOT","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2008.08826","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}