{"url":"/task/object-tracking","name":"Object Tracking","slug":"object-tracking","description_markdown":"**Object tracking** is the task of taking an initial set of object detections, creating a unique ID for each of the initial detections, and then tracking each of the objects as they move around frames in a video, maintaining the ID assignment. State-of-the-art methods involve fusing data from RGB and event-based cameras to produce more reliable object tracking. CNN-based models using only RGB images as input are also effective. The most popular benchmark is OTB. There are several evaluation metrics specific to object tracking, including HOTA, MOTA, IDF1, and Track-mAP. \r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [Towards-Realtime-MOT\r\n](https://github.com/Zhongdao/Towards-Realtime-MOT) )</span>","categories":[{"name":"Computer Vision","url":"/area/computer-vision"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":1966,"papers_with_code":767,"benchmarks":10,"benchmark_tables_in_archive":10,"benchmark_tables_shown":10,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":69,"subtasks":10,"parent_tasks":0},"benchmarks":[{"leaderboard":"/sota/object-tracking-on-coesot","slug":"object-tracking-on-coesot","dataset":"COESOT","dataset_url":"/dataset/coesot","rows_in_archive":12,"metrics":["Success Rate","Precision Rate"],"first_row_in_archive_order":{"model":"HR-CEUTrack-Large","paper_title":"Cross-modal Orthogonal High-rank Augmentation for RGB-Event Transformer-trackers","paper_url":"/paper/cross-modal-orthogonal-high-rank-augmentation","paper_date":"2023-07-09","arxiv_id":"2307.04129","code_links":[{"title":"zhu-zhiyu/nvs_solver","url":"https://github.com/zhu-zhiyu/nvs_solver"},{"title":"ZHU-Zhiyu/High-Rank_RGB-Event_Tracker","url":"https://github.com/ZHU-Zhiyu/High-Rank_RGB-Event_Tracker"}],"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}}},{"leaderboard":"/sota/object-tracking-on-fe108","slug":"object-tracking-on-fe108","dataset":"FE108","dataset_url":"/dataset/fe108","rows_in_archive":8,"metrics":["Success Rate","Averaged Precision"],"first_row_in_archive_order":{"model":"HR-MonTrack-Base","paper_title":"Cross-modal Orthogonal High-rank Augmentation for RGB-Event Transformer-trackers","paper_url":"/paper/cross-modal-orthogonal-high-rank-augmentation","paper_date":"2023-07-09","arxiv_id":"2307.04129","code_links":[{"title":"zhu-zhiyu/nvs_solver","url":"https://github.com/zhu-zhiyu/nvs_solver"},{"title":"ZHU-Zhiyu/High-Rank_RGB-Event_Tracker","url":"https://github.com/ZHU-Zhiyu/High-Rank_RGB-Event_Tracker"}],"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}}},{"leaderboard":"/sota/object-tracking-on-quadtrack","slug":"object-tracking-on-quadtrack","dataset":"QuadTrack","dataset_url":"/dataset/quadtrack","rows_in_archive":8,"metrics":["HOTA"],"first_row_in_archive_order":{"model":"OmniTrack","paper_title":"Omnidirectional Multi-Object Tracking","paper_url":"/paper/omnidirectional-multi-object-tracking","paper_date":"2025-03-06","arxiv_id":"2503.04565","code_links":[{"title":"xifen523/omnitrack","url":"https://github.com/xifen523/omnitrack"}],"syntology":null}},{"leaderboard":"/sota/object-tracking-on-seadronessee","slug":"object-tracking-on-seadronessee","dataset":"SeaDronesSee","dataset_url":"/dataset/seadronessee","rows_in_archive":5,"metrics":["Success Rate","Precision Score"],"first_row_in_archive_order":{"model":"DiMP50","paper_title":"SeaDronesSee: A Maritime Benchmark for Detecting Humans in Open Water","paper_url":"/paper/seadronessee-a-maritime-benchmark-for","paper_date":"2021-05-05","arxiv_id":"2105.01922","code_links":[],"syntology":null}},{"leaderboard":"/sota/object-tracking-on-birdsai-icvgip-2020","slug":"object-tracking-on-birdsai-icvgip-2020","dataset":"BIRDSAI - ICVGIP 2020","dataset_url":null,"rows_in_archive":4,"metrics":["Humans","Animals"],"first_row_in_archive_order":{"model":"final","paper_title":null,"paper_url":null,"paper_date":"","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/object-tracking-on-kitti","slug":"object-tracking-on-kitti","dataset":"KITTI","dataset_url":"/dataset/kitti","rows_in_archive":2,"metrics":["mean precision","mean success"],"first_row_in_archive_order":{"model":"M2-Track","paper_title":"Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds","paper_url":"/paper/beyond-3d-siamese-tracking-a-motion-centric","paper_date":"2022-03-03","arxiv_id":"2203.01730","code_links":[{"title":"ghostish/open3dsot","url":"https://github.com/ghostish/open3dsot"}],"syntology":null}},{"leaderboard":"/sota/object-tracking-on-mmptrack","slug":"object-tracking-on-mmptrack","dataset":"MMPTRACK","dataset_url":"/dataset/mmptrack","rows_in_archive":2,"metrics":["3DMOTA"],"first_row_in_archive_order":{"model":"UMMT","paper_title":"A Unified Multi-view Multi-person Tracking Framework","paper_url":"/paper/a-unified-multi-view-multi-person-tracking","paper_date":"2023-02-08","arxiv_id":"2302.03820","code_links":[],"syntology":null}},{"leaderboard":"/sota/object-tracking-on-1","slug":"object-tracking-on-1","dataset":"1","dataset_url":"/dataset/1-1","rows_in_archive":1,"metrics":["0S"],"first_row_in_archive_order":{"model":"YOLOv7","paper_title":"CholecTrack20: A Dataset for Multi-Class Multiple Tool Tracking in Laparoscopic Surgery","paper_url":"/paper/cholectrack20-a-dataset-for-multi-class","paper_date":"2023-12-12","arxiv_id":"2312.07352","code_links":[{"title":"camma-public/cholectrack20","url":"https://github.com/camma-public/cholectrack20"}],"syntology":null}},{"leaderboard":"/sota/object-tracking-on-perception-test","slug":"object-tracking-on-perception-test","dataset":"Perception Test","dataset_url":"/dataset/perception-test","rows_in_archive":1,"metrics":["Average IOU"],"first_row_in_archive_order":{"model":"Siam-FC","paper_title":"Perception Test: A Diagnostic Benchmark for Multimodal Video Models","paper_url":"/paper/perception-test-a-diagnostic-benchmark-for-2","paper_date":"2023-05-23","arxiv_id":"2305.13786","code_links":[{"title":"deepmind/perception_test","url":"https://github.com/deepmind/perception_test"}],"syntology":null}},{"leaderboard":"/sota/object-tracking-on-visevent","slug":"object-tracking-on-visevent","dataset":"VisEvent","dataset_url":"/dataset/visevent","rows_in_archive":1,"metrics":["Precision Plot"],"first_row_in_archive_order":{"model":"RT-MDNet","paper_title":"VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows","paper_url":"/paper/visevent-reliable-object-tracking-via","paper_date":"2021-08-11","arxiv_id":"2108.05015","code_links":[{"title":"wangxiao5791509/VisEvent_SOT_Benchmark","url":"https://github.com/wangxiao5791509/VisEvent_SOT_Benchmark"},{"title":"wangxiao5791509/RGB-DVS-SOT-Baselines","url":"https://github.com/wangxiao5791509/RGB-DVS-SOT-Baselines"}],"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}}}],"datasets":[{"url":"/dataset/kitti","name":"KITTI","full_name":"","num_papers_in_archive":3661},{"url":"/dataset/lasot","name":"LaSOT","full_name":"Large-scale Single Object Tracking","num_papers_in_archive":275},{"url":"/dataset/got-10k","name":"GOT-10k","full_name":"Generic Object Tracking Benchmark","num_papers_in_archive":239},{"url":"/dataset/motchallenge","name":"MOTChallenge","full_name":"","num_papers_in_archive":192},{"url":"/dataset/virtual-kitti","name":"Virtual KITTI","full_name":"","num_papers_in_archive":133},{"url":"/dataset/vot2018","name":"VOT2018","full_name":"VOT2018","num_papers_in_archive":129},{"url":"/dataset/uavdt","name":"UAVDT","full_name":"Unmanned Aerial Vehicle Benchmark Object Detection and Tracking","num_papers_in_archive":96},{"url":"/dataset/vot2017","name":"VOT2017","full_name":"Visual Object Tracking Challenge","num_papers_in_archive":56},{"url":"/dataset/ua-detrac","name":"UA-DETRAC","full_name":"","num_papers_in_archive":53},{"url":"/dataset/virtual-kitti-2","name":"Virtual KITTI 2","full_name":"","num_papers_in_archive":53},{"url":"/dataset/event-camera-dataset","name":"Event-Camera Dataset","full_name":"","num_papers_in_archive":51},{"url":"/dataset/tao","name":"TAO","full_name":"Tracking Any Object Dataset","num_papers_in_archive":49},{"url":"/dataset/objectron","name":"Objectron","full_name":null,"num_papers_in_archive":48},{"url":"/dataset/vot","name":"VOTChallenge","full_name":"Visual Object Tracking","num_papers_in_archive":36},{"url":"/dataset/oxuva","name":"OxUva","full_name":"","num_papers_in_archive":34},{"url":"/dataset/1-1","name":"1","full_name":"","num_papers_in_archive":28},{"url":"/dataset/kitti-mots","name":"KITTI MOTS","full_name":"KITTI Multi-Object Tracking and Segmentation (MOTS) Evaluation","num_papers_in_archive":28},{"url":"/dataset/okutama-action","name":"Okutama-Action","full_name":"","num_papers_in_archive":24},{"url":"/dataset/visevent","name":"VisEvent","full_name":"","num_papers_in_archive":23},{"url":"/dataset/hieve","name":"HiEve","full_name":"Human-in-Events","num_papers_in_archive":19},{"url":"/dataset/seadronessee","name":"SeaDronesSee","full_name":"SeaDronesSee: A Maritime Benchmark for Detecting Humans in Open Water","num_papers_in_archive":18},{"url":"/dataset/cdtb","name":"CDTB","full_name":"Color-and-Depth Tracking","num_papers_in_archive":17},{"url":"/dataset/ufpr-alpr","name":"UFPR-ALPR","full_name":"","num_papers_in_archive":15},{"url":"/dataset/tlp","name":"TLP","full_name":"Track Long and Prosper","num_papers_in_archive":14},{"url":"/dataset/fe108","name":"FE108","full_name":"","num_papers_in_archive":13},{"url":"/dataset/vot2014","name":"VOT2014","full_name":"Visual Object Tracking Challenge 2014","num_papers_in_archive":12},{"url":"/dataset/2024-ai-city-challenge-mtmc-people-tracking","name":"2024 AI City Challenge","full_name":"","num_papers_in_archive":10},{"url":"/dataset/perception-test","name":"Perception Test","full_name":"","num_papers_in_archive":10},{"url":"/dataset/coesot","name":"COESOT","full_name":"","num_papers_in_archive":9},{"url":"/dataset/pathtrack","name":"PathTrack","full_name":"","num_papers_in_archive":9},{"url":"/dataset/ptb-tir","name":"PTB-TIR","full_name":"","num_papers_in_archive":9},{"url":"/dataset/quadtrack","name":"QuadTrack","full_name":"","num_papers_in_archive":9},{"url":"/dataset/dolphins","name":"DOLPHINS","full_name":"Dataset for Collaborative Perception enabled Harmonious and Interconnected Self-driving","num_papers_in_archive":7},{"url":"/dataset/trek150","name":"TREK-150","full_name":"","num_papers_in_archive":7},{"url":"/dataset/mmptrack","name":"MMPTRACK","full_name":"Multi-camera Multiple People Tracking Dataset","num_papers_in_archive":6},{"url":"/dataset/videocube","name":"VideoCube","full_name":"","num_papers_in_archive":6},{"url":"/dataset/aimotive-dataset","name":"aiMotive Dataset","full_name":"aiMotive Multimodal Dataset","num_papers_in_archive":5},{"url":"/dataset/rf100","name":"RF100","full_name":"Roboflow 100","num_papers_in_archive":5},{"url":"/dataset/homer","name":"HOMER","full_name":"Household Object Movements from Everyday Routines","num_papers_in_archive":4},{"url":"/dataset/mdot","name":"MDOT","full_name":"","num_papers_in_archive":4},{"url":"/dataset/vot2019","name":"VOT2019","full_name":null,"num_papers_in_archive":4},{"url":"/dataset/wisdom","name":"WISDOM","full_name":"Warehouse Instance Segmentation Dataset for Object Manipulation","num_papers_in_archive":4},{"url":"/dataset/3d-pop","name":"3D-POP","full_name":"","num_papers_in_archive":3},{"url":"/dataset/cfc","name":"CFC","full_name":"Caltech Fish Counting Dataset","num_papers_in_archive":3},{"url":"/dataset/divotrack","name":"DIVOTrack","full_name":"","num_papers_in_archive":3},{"url":"/dataset/pred18","name":"PRED18","full_name":"PRED18: Predator/Prey DAVIS Dataset","num_papers_in_archive":3},{"url":"/dataset/uav-gesture","name":"UAV-GESTURE","full_name":"","num_papers_in_archive":3},{"url":"/dataset/au-air","name":"AU-AIR","full_name":null,"num_papers_in_archive":2},{"url":"/dataset/cholectrack20","name":"CholecTrack20","full_name":"Multi-Perspective Multi-Class Multi-Object Tracking Dataset For Surgical Tools","num_papers_in_archive":2},{"url":"/dataset/dttd","name":"DTTD","full_name":"","num_papers_in_archive":2},{"url":"/dataset/itb","name":"ITB","full_name":"Informative Tracking Benchmark","num_papers_in_archive":2},{"url":"/dataset/lindenthal-camera-traps","name":"Lindenthal Camera Traps","full_name":"","num_papers_in_archive":2},{"url":"/dataset/mphoi-72","name":"MPHOI-72","full_name":"Multi-person Human-object Interaction Dataset 72","num_papers_in_archive":2},{"url":"/dataset/open-radar-datasets","name":"Open Radar Datasets","full_name":"Open Radar Datasets: Outdoor Moving Object Dataset","num_papers_in_archive":2},{"url":"/dataset/personpath22","name":"PersonPath22","full_name":"","num_papers_in_archive":2},{"url":"/dataset/pesmod","name":"PESMOD","full_name":"PExels Small Moving Object Detection","num_papers_in_archive":2},{"url":"/dataset/biodrone","name":"BioDrone","full_name":"","num_papers_in_archive":1},{"url":"/dataset/cope-119","name":"COPE-119","full_name":"","num_papers_in_archive":1},{"url":"/dataset/ht1080wt-cells-3d-collagen-type-i-matrices","name":"HT1080WT cells - 3D collagen type I matrices","full_name":"HT1080WT cells embedded in 3D collagen type I matrices - manual annotations for cell instance segmentation and tracking","num_papers_in_archive":1},{"url":"/dataset/mobiface","name":"MobiFace","full_name":"MobiFace","num_papers_in_archive":1},{"url":"/dataset/omni-mot","name":"Omni-MOT","full_name":"","num_papers_in_archive":1},{"url":"/dataset/remote-flash-lidar-vehicles-dataset","name":"Remote Flash LiDAR Vehicles Dataset","full_name":"","num_papers_in_archive":1},{"url":"/dataset/rmot-223","name":"RMOT-223","full_name":"","num_papers_in_archive":1},{"url":"/dataset/s-odv2","name":"S-ODv2","full_name":"SeaDronesSee-Object Detection v2","num_papers_in_archive":1},{"url":"/dataset/sfu-hw-tracks","name":"SFU-HW-Tracks","full_name":"","num_papers_in_archive":1},{"url":"/dataset/sotverse","name":"SOTVerse","full_name":"","num_papers_in_archive":1},{"url":"/dataset/synoclip","name":"SynoClip","full_name":"","num_papers_in_archive":1},{"url":"/dataset/timbervision-dataset","name":"TimberVision","full_name":"TimberVision","num_papers_in_archive":1},{"url":"/dataset/trek-100","name":"TREK-100","full_name":"","num_papers_in_archive":1}],"subtasks":[{"url":"/task/amodal-tracking","name":"Amodal Tracking"},{"url":"/task/cell-tracking","name":"Cell Tracking"},{"url":"/task/multi-object-tracking","name":"Multi-Object Tracking"},{"url":"/task/multiple-object-tracking","name":"Multiple Object Tracking"},{"url":"/task/online-multi-object-tracking","name":"Online Multi-Object Tracking"},{"url":"/task/pupil-tracking","name":"Pupil Tracking"},{"url":"/task/sports-ball-detection-and-tracking","name":"Sports Ball Detection and Tracking"},{"url":"/task/thermal-infrared-object-tracking","name":"Thermal Infrared Object Tracking"},{"url":"/task/video-object-tracking","name":"Video Object Tracking"},{"url":"/task/visual-object-tracking","name":"Visual Object Tracking"}],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":767,"tagged_in_all":1966,"items":[{"url":"/paper/simple-online-and-realtime-tracking-with-a","title":"Simple Online and Realtime Tracking with a Deep Association Metric","date":"2017-03-21","arxiv_id":"1703.07402","repositories_listed":75,"syntology":{"n":40,"n_ran":12,"n_unverified":28,"n_pointer_only":7}},{"url":"/paper/a-simple-baseline-for-multi-object-tracking","title":"FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking","date":"2020-04-04","arxiv_id":"2004.01888","repositories_listed":33,"syntology":{"n":53,"n_ran":8,"n_unverified":45,"n_pointer_only":0}},{"url":"/paper/strongsort-make-deepsort-great-again","title":"StrongSORT: Make DeepSORT Great Again","date":"2022-02-28","arxiv_id":"2202.13514","repositories_listed":14,"syntology":{"n":20,"n_ran":3,"n_unverified":17,"n_pointer_only":2}},{"url":"/paper/center-based-3d-object-detection-and-tracking","title":"Center-based 3D Object Detection and Tracking","date":"2020-06-19","arxiv_id":"2006.11275","repositories_listed":13,"syntology":{"n":22,"n_ran":7,"n_unverified":15,"n_pointer_only":0}},{"url":"/paper/tracking-without-bells-and-whistles","title":"Tracking without bells and whistles","date":"2019-03-13","arxiv_id":"1903.05625","repositories_listed":13,"syntology":{"n":14,"n_ran":3,"n_unverified":11,"n_pointer_only":2}},{"url":"/paper/towards-real-time-multi-object-tracking","title":"Towards Real-Time Multi-Object Tracking","date":"2019-09-27","arxiv_id":"1909.12605","repositories_listed":12,"syntology":{"n":36,"n_ran":2,"n_unverified":34,"n_pointer_only":0}},{"url":"/paper/re3-real-time-recurrent-regression-networks","title":"Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects","date":"2017-05-17","arxiv_id":"1705.06368","repositories_listed":11,"syntology":null},{"url":"/paper/bytetrack-multi-object-tracking-by-1","title":"ByteTrack: Multi-Object Tracking by Associating Every Detection Box","date":"2021-10-13","arxiv_id":"2110.06864","repositories_listed":10,"syntology":{"n":12,"n_ran":1,"n_unverified":11,"n_pointer_only":0}},{"url":"/paper/fully-convolutional-siamese-networks-for-1","title":"Fully-Convolutional Siamese Networks for Object Tracking","date":"2016-06-30","arxiv_id":"1606.09549","repositories_listed":10,"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/mot16-a-benchmark-for-multi-object-tracking","title":"MOT16: A Benchmark for Multi-Object Tracking","date":"2016-03-02","arxiv_id":"1603.00831","repositories_listed":8,"syntology":{"n":38,"n_ran":11,"n_unverified":27,"n_pointer_only":1}},{"url":"/paper/soccernet-2022-challenges-results","title":"SoccerNet 2022 Challenges Results","date":"2022-10-05","arxiv_id":"2210.02365","repositories_listed":7,"syntology":null},{"url":"/paper/bot-sort-robust-associations-multi-pedestrian","title":"BoT-SORT: Robust Associations Multi-Pedestrian Tracking","date":"2022-06-29","arxiv_id":"2206.14651","repositories_listed":7,"syntology":{"n":10,"n_ran":4,"n_unverified":6,"n_pointer_only":1}},{"url":"/paper/observation-centric-sort-rethinking-sort-for","title":"Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking","date":"2022-03-27","arxiv_id":"2203.14360","repositories_listed":7,"syntology":{"n":31,"n_ran":15,"n_unverified":16,"n_pointer_only":2}},{"url":"/paper/simpletrack-rethinking-and-improving-the-jde","title":"SimpleTrack: Rethinking and Improving the JDE Approach for Multi-Object Tracking","date":"2022-03-08","arxiv_id":"2203.03985","repositories_listed":6,"syntology":null},{"url":"/paper/awesome-multi-modal-object-tracking","title":"Awesome Multi-modal Object Tracking","date":"2024-05-23","arxiv_id":"2405.14200","repositories_listed":5,"syntology":null},{"url":"/paper/trasw-tracklet-switch-adversarial-attacks","title":"Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers","date":"2021-11-17","arxiv_id":"2111.08954","repositories_listed":5,"syntology":null},{"url":"/paper/rethinking-self-supervised-correspondence","title":"Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective","date":"2021-03-31","arxiv_id":"2103.17263","repositories_listed":5,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/defmo-deblurring-and-shape-recovery-of-fast","title":"DeFMO: Deblurring and Shape Recovery of Fast Moving Objects","date":"2020-12-01","arxiv_id":"2012.00595","repositories_listed":5,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/hota-a-higher-order-metric-for-evaluating","title":"HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking","date":"2020-09-16","arxiv_id":"2009.07736","repositories_listed":5,"syntology":{"n":10,"n_ran":2,"n_unverified":8,"n_pointer_only":0}},{"url":"/paper/high-performance-visual-tracking-with-siamese","title":"High Performance Visual Tracking With Siamese Region Proposal Network","date":"2018-06-01","arxiv_id":null,"repositories_listed":5,"syntology":null},{"url":"/paper/dcfnet-discriminant-correlation-filters","title":"DCFNet: Discriminant Correlation Filters Network for Visual Tracking","date":"2017-04-13","arxiv_id":"1704.04057","repositories_listed":5,"syntology":null},{"url":"/paper/long-term-frame-event-visual-tracking","title":"Long-term Frame-Event Visual Tracking: Benchmark Dataset and Baseline","date":"2024-03-09","arxiv_id":"2403.05839","repositories_listed":4,"syntology":null},{"url":"/paper/event-stream-based-visual-object-tracking-a","title":"Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline","date":"2023-09-26","arxiv_id":"2309.14611","repositories_listed":4,"syntology":{"n":9,"n_ran":9,"n_unverified":0,"n_pointer_only":9}},{"url":"/paper/motrv2-bootstrapping-end-to-end-multi-object","title":"MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors","date":"2022-11-17","arxiv_id":"2211.09791","repositories_listed":4,"syntology":null},{"url":"/paper/procontext-exploring-progressive-context","title":"ProContEXT: Exploring Progressive Context Transformer for Tracking","date":"2022-10-27","arxiv_id":"2210.15511","repositories_listed":4,"syntology":null},{"url":"/paper/improving-object-detection-multi-object","title":"Improving Object Detection, Multi-object Tracking, and Re-Identification for Disaster Response Drones","date":"2022-01-05","arxiv_id":"2201.01494","repositories_listed":4,"syntology":null},{"url":"/paper/model-free-vehicle-tracking-and-state","title":"Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences","date":"2021-03-10","arxiv_id":"2103.06028","repositories_listed":4,"syntology":null},{"url":"/paper/hardnet-mseg-a-simple-encoder-decoder-polyp","title":"HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS","date":"2021-01-18","arxiv_id":"2101.07172","repositories_listed":4,"syntology":null},{"url":"/paper/rethinking-the-competition-between-detection","title":"Rethinking the competition between detection and ReID in Multi-Object Tracking","date":"2020-10-23","arxiv_id":"2010.12138","repositories_listed":4,"syntology":{"n":15,"n_ran":2,"n_unverified":13,"n_pointer_only":0}},{"url":"/paper/lidartag-a-real-time-fiducial-tag-using-point","title":"LiDARTag: A Real-Time Fiducial Tag System for Point Clouds","date":"2019-08-23","arxiv_id":"1908.10349","repositories_listed":4,"syntology":null}],"syntology_records":16,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}