{"url":"/dataset/mot16","name":"MOT16","full_name":"Multiple Object Tracking 2016","description_markdown":"The **MOT16** dataset is a dataset for multiple object tracking. It a collection of existing and new data (part of the sources are from and ), containing 14 challenging real-world videos of both static scenes and moving scenes, 7 for training and 7 for testing. It is a large-scale dataset, composed of totally 110407 bounding boxes in training set and 182326 bounding boxes in test set. All video sequences are annotated under strict standards, their ground-truths are highly accurate, making the evaluation meaningful.\r\n\r\nSource: [SOT for MOT](https://arxiv.org/abs/1712.01059)\r\nImage Source: [https://www.researchgate.net/figure/Sample-results-on-the-sequence-MOT16-07-encoded-as-in-the-previous-figure-Table-1_fig3_309641746](https://www.researchgate.net/figure/Sample-results-on-the-sequence-MOT16-07-encoded-as-in-the-previous-figure-Table-1_fig3_309641746)","description_withheld":null,"homepage":"https://motchallenge.net/data/MOT16/","introduced_date":"2016-03-02","introduced_date_note":null,"introduced_by":{"paper":"/paper/mot16-a-benchmark-for-multi-object-tracking","title":"MOT16: A Benchmark for Multi-Object Tracking","first_author":"Anton Milan","url":null},"license":{"name":"CC BY-NC-SA 3.0","url":"https://motchallenge.net/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Multi-Object Tracking","url":"/task/multi-object-tracking","datasets_with_task":"/datasets/task/multi-object-tracking"},{"name":"Online Multi-Object Tracking","url":"/task/online-multi-object-tracking","datasets_with_task":"/datasets/task/online-multi-object-tracking"}],"languages":[],"variants":["MOT16"],"data_loaders":[{"repo":"https://github.com/open-mmlab/mmtracking","url":"https://github.com/open-mmlab/mmtracking/blob/master/docs/dataset.md","frameworks":["pytorch"]}],"num_papers_in_archive":149,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/multi-object-tracking-on-mot16","task":"Multi-Object Tracking","dataset_variant":"MOT16","rows":24,"metrics":["MOTA","IDF1","IDs"],"first_row_in_archive_order":{"model":"PPTracking","paper":"/paper/pp-yoloe-an-evolved-version-of-yolo","metrics":{"MOTA":"77.7"},"code_links":[{"title":"PaddlePaddle/PaddleDetection","url":"https://github.com/PaddlePaddle/PaddleDetection"},{"title":"open-mmlab/mmyolo","url":"https://github.com/open-mmlab/mmyolo"},{"title":"PaddlePaddle/PaddleYOLO","url":"https://github.com/PaddlePaddle/PaddleYOLO"},{"title":"Nioolek/PPYOLOE_pytorch","url":"https://github.com/Nioolek/PPYOLOE_pytorch"},{"title":"Gaurav14cs17/YOLOE","url":"https://github.com/Gaurav14cs17/YOLOE"},{"title":"CycloneBoy/PPDetectionPytorch","url":"https://github.com/CycloneBoy/PPDetectionPytorch"},{"title":"2023-MindSpore-4/Code10","url":"https://github.com/2023-MindSpore-4/Code10/tree/main/YOLOv5"},{"title":"Mind23-2/MindCode-130","url":"https://github.com/Mind23-2/MindCode-130"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/online-multi-object-tracking-on-mot16","task":"Online Multi-Object Tracking","dataset_variant":"MOT16","rows":5,"metrics":["MOTA"],"first_row_in_archive_order":{"model":"PP-Tracking","paper":"/paper/pp-yoloe-an-evolved-version-of-yolo","metrics":{"MOTA":"77.7"},"code_links":[{"title":"PaddlePaddle/PaddleDetection","url":"https://github.com/PaddlePaddle/PaddleDetection"},{"title":"open-mmlab/mmyolo","url":"https://github.com/open-mmlab/mmyolo"},{"title":"PaddlePaddle/PaddleYOLO","url":"https://github.com/PaddlePaddle/PaddleYOLO"},{"title":"Nioolek/PPYOLOE_pytorch","url":"https://github.com/Nioolek/PPYOLOE_pytorch"},{"title":"Gaurav14cs17/YOLOE","url":"https://github.com/Gaurav14cs17/YOLOE"},{"title":"CycloneBoy/PPDetectionPytorch","url":"https://github.com/CycloneBoy/PPDetectionPytorch"},{"title":"2023-MindSpore-4/Code10","url":"https://github.com/2023-MindSpore-4/Code10/tree/main/YOLOv5"},{"title":"Mind23-2/MindCode-130","url":"https://github.com/Mind23-2/MindCode-130"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/hoptrack-a-real-time-multi-object-tracking","title":"HopTrack: A Real-time Multi-Object Tracking System for Embedded Devices","date":"2024-11-01","rows_on_this_dataset":1,"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","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/detection-recovery-in-online-multi-object","title":"Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker","date":"2022-05-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/pp-yoloe-an-evolved-version-of-yolo","title":"PP-YOLOE: An evolved version of YOLO","date":"2022-03-30","rows_on_this_dataset":2,"code_links":8,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":27,"samples_ran":5,"samples_unverified":22,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/online-multi-object-tracking-with","title":"Online Multi-Object Tracking with Unsupervised Re-Identification Learning and Occlusion Estimation","date":"2022-01-04","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/do-different-tracking-tasks-require-different","title":"Do Different Tracking Tasks Require Different Appearance Models?","date":"2021-07-05","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":17,"samples_ran":6,"samples_unverified":11,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/motr-end-to-end-multiple-object-tracking-with","title":"MOTR: End-to-End Multiple-Object Tracking with Transformer","date":"2021-05-07","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/spatial-temporal-graph-transformer-for","title":"TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking","date":"2021-04-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/track-to-detect-and-segment-an-online-multi","title":"Track to Detect and Segment: An Online Multi-Object Tracker","date":"2021-03-16","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deft-detection-embeddings-for-tracking","title":"DEFT: Detection Embeddings for Tracking","date":"2021-02-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/remots-self-supervised-refining-multi-object","title":"ReMOTS: Self-Supervised Refining Multi-Object Tracking and Segmentation","date":"2020-07-07","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/lifted-disjoint-paths-with-application-in-1","title":"Lifted Disjoint Paths with Application in Multiple Object Tracking","date":"2020-06-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/joint-detection-and-multi-object-tracking","title":"Joint Object Detection and Multi-Object Tracking with Graph Neural Networks","date":"2020-06-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/quasi-dense-instance-similarity-learning","title":"Quasi-Dense Similarity Learning for Multiple Object Tracking","date":"2020-06-11","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/learning-a-neural-solver-for-multiple-object-1","title":"Learning a Neural Solver for Multiple Object Tracking","date":"2020-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/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","rows_on_this_dataset":1,"code_links":33,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":53,"samples_ran":8,"samples_unverified":45,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/towards-real-time-multi-object-tracking","title":"Towards Real-Time Multi-Object Tracking","date":"2019-09-27","rows_on_this_dataset":1,"code_links":12,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":36,"samples_ran":2,"samples_unverified":34,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deepmot-a-differentiable-framework-for","title":"How To Train Your Deep Multi-Object Tracker","date":"2019-06-15","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/tracking-without-bells-and-whistles","title":"Tracking without bells and whistles","date":"2019-03-13","rows_on_this_dataset":1,"code_links":13,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":3,"samples_unverified":11,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/online-multi-object-tracking-with-dual","title":"Online Multi-Object Tracking with Dual Matching Attention Networks","date":"2019-02-02","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/exploit-the-connectivity-multi-object","title":"Exploit the Connectivity: Multi-Object Tracking with TrackletNet","date":"2018-11-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/real-time-multiple-people-tracking-with","title":"Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification","date":"2018-09-12","rows_on_this_dataset":2,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":0,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/trajectory-factory-tracklet-cleaving-and-re","title":"Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking","date":"2018-04-12","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/fusion-of-head-and-full-body-detectors-for","title":"Fusion of Head and Full-Body Detectors for Multi-Object Tracking","date":"2017-05-23","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/near-online-multi-target-tracking-with","title":"Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor","date":"2015-04-09","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":7,"samples_harvested":157,"samples_ran":28,"samples_unverified":129,"pointer_only_for_licence":2,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}