mlsanityData to 14 Oct 2026

MOT16: A Benchmark for Multi-Object Tracking

arXiv 1603.00831 ↗First posted 2 Mar 2016cs.CVcited by 532

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Abstract (arXiv)

Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for reseach. Recently, a new benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal of collecting existing and new data and creating a framework for the standardized evaluation of multiple object tracking methods. The first release of the benchmark focuses on multiple people tracking, since pedestrians are by far the most studied object in the tracking community. This paper accompanies a new release of the MOTChallenge benchmark. Unlike the initial release, all videos of MOT16 have been carefully annotated following a consistent protocol. Moreover, it not only offers a significant increase in the number of labeled boxes, but also provides multiple object classes beside pedestrians and the level of visibility for every single object of interest.

Key facts. Cited by 532 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Mar 2016.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 81; 2022: 116; 2023: 110; 2024: 120; 2025: 63; 2026: 41.

532 papers.

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  1. 2021ByteTrack: Multi-Object Tracking by Associating Every Detection Boxcited by 823
  2. 2021TrackFormer: Multi-Object Tracking with Transformerscited by 518
  3. 2021MOTR: End-to-End Multiple-Object Tracking with Transformercited by 381
  4. 2022DiffusionDet: Diffusion Model for Object Detectioncited by 354
  5. 2022Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Trackingcited by 346
  6. 2022BoT-SORT: Robust Associations Multi-Pedestrian Trackingcited by 320
  7. 2022TAP-Vid: A Benchmark for Tracking Any Point in a Videocited by 263
  8. 2022NeuMan: Neural Human Radiance Field from a Single Videocited by 244
  9. 2021Associating Objects with Transformers for Video Object Segmentationcited by 226
  10. 2021DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motioncited by 217
  11. 2022StrongSORT: Make DeepSORT Great Againcited by 213
  12. 2023Video Understanding with Large Language Models: A Surveycited by 206
38 more
  1. 2021Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentationcited by 203
  2. 2022AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Timecited by 200
  3. 2023Universal Instance Perception as Object Discovery and Retrievalcited by 192
  4. 2021Track to Detect and Segment: An Online Multi-Object Trackercited by 156
  5. 2021Occluded Video Instance Segmentation: A Benchmarkcited by 154
  6. 2022DETRs with Hybrid Matchingcited by 150
  7. 2022MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectorscited by 148
  8. 2021A Survey on Deep Learning Technique for Video Segmentationcited by 132
  9. 2023Deep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-Identificationcited by 132
  10. 2023SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenescited by 131
  11. 2021Learning to Track with Object Permanencecited by 119
  12. 2022MeMOT: Multi-Object Tracking with Memorycited by 114
  13. 2022Global Tracking Transformerscited by 110
  14. 2021TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Trackingcited by 93
  15. 2022Towards Grand Unification of Object Trackingcited by 93
  16. 2023MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Trackingcited by 88
  17. 2024Streaming Dense Video Captioningcited by 82
  18. 2021Monocular Quasi-Dense 3D Object Trackingcited by 81
  19. 2022SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videoscited by 80
  20. 2021Neural Production Systems: Learning Rule-Governed Visual Dynamicscited by 75
  21. 2021STEP: Segmenting and Tracking Every Pixelcited by 73
  22. 2021Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directionscited by 73
  23. 2021MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?cited by 69
  24. 2021Multiple Object Tracking with Correlation Learningcited by 67
  25. 2023Hybrid-SORT: Weak Cues Matter for Online Multi-Object Trackingcited by 67
  26. 2022Hard to Track Objects with Irregular Motions and Similar Appearances? Make It Easier by Buffering the Matching Spacecited by 66
  27. 2023MotionTrack: Learning Robust Short-term and Long-term Motions for Multi-Object Trackingcited by 64
  28. 2021SiamMOT: Siamese Multi-Object Trackingcited by 62
  29. 2021Do Different Tracking Tasks Require Different Appearance Models?cited by 62
  30. 2021SeaDronesSee: A Maritime Benchmark for Detecting Humans in Open Watercited by 61
  31. 2022QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Trackingcited by 61
  32. 2024Multiple Object Tracking as ID Predictioncited by 60
  33. 2024DiffMOT: A Real-time Diffusion-based Multiple Object Tracker with Non-linear Predictioncited by 59
  34. 2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
  35. 2021DEFT: Detection Embeddings for Trackingcited by 56
  36. 2021OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Associationcited by 55
  37. 2021RelationTrack: Relation-aware Multiple Object Tracking with Decoupled Representationcited by 52
  38. 2022CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusioncited by 50

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