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The Progression of Transformers from Language to Vision to MOT: A Literature Review on Multi-Object Tracking with Transformers

arXiv 2406.16784 ↗First posted 24 Jun 2024cs.CV cs.AIcited by 0

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Abstract (arXiv)

The transformer neural network architecture allows for autoregressive sequence-to-sequence modeling through the use of attention layers. It was originally created with the application of machine translation but has revolutionized natural language processing. Recently, transformers have also been applied across a wide variety of pattern recognition tasks, particularly in computer vision. In this literature review, we describe major advances in computer vision utilizing transformers. We then focus specifically on Multi-Object Tracking (MOT) and discuss how transformers are increasingly becoming competitive in state-of-the-art MOT works, yet still lag behind traditional deep learning methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 43 of its 55 references that are in the map. First posted on arXiv 24 Jun 2024.

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43 of 55 references.

References · 43

  1. 2017Attention Is All You Needcited by 59,932
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  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  5. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  6. 2020End-to-End Object Detection with Transformerscited by 7,384
  7. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  8. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  9. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  10. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  11. 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,848
  12. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
31 more
  1. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  2. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  3. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,282
  4. 2019Objects as Pointscited by 1,172
  5. 2016Simple Online and Realtime Trackingcited by 862
  6. 2021ByteTrack: Multi-Object Tracking by Associating Every Detection Boxcited by 812
  7. 2021Florence: A New Foundation Model for Computer Visioncited by 802
  8. 2019Big Transfer (BiT): General Visual Representation Learningcited by 766
  9. 2016Fully-Convolutional Siamese Networks for Object Trackingcited by 566
  10. 2017Convolutional Sequence to Sequence Learningcited by 550
  11. 2020HOTA: A Higher Order Metric for Evaluating Multi-Object Trackingcited by 546
  12. 2016MOT16: A Benchmark for Multi-Object Trackingcited by 531
  13. 2021TrackFormer: Multi-Object Tracking with Transformerscited by 515
  14. 2020Tracking Objects as Pointscited by 483
  15. 2019Tracking without bells and whistlescited by 382
  16. 2021MOTR: End-to-End Multiple-Object Tracking with Transformercited by 379
  17. 2020TransTrack: Multiple Object Tracking with Transformercited by 370
  18. 2020MOT20: A benchmark for multi object tracking in crowded scenescited by 355
  19. 2022Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Trackingcited by 345
  20. 2022BoT-SORT: Robust Associations Multi-Pedestrian Trackingcited by 313
  21. 2019Towards Real-Time Multi-Object Trackingcited by 311
  22. 2022StrongSORT: Make DeepSORT Great Againcited by 213
  23. 2014Multiple Object Tracking: A Literature Reviewcited by 165
  24. 2020TAO: A Large-Scale Benchmark for Tracking Any Objectcited by 146
  25. 2021Learning to Track with Object Permanencecited by 117
  26. 2022Global Tracking Transformerscited by 110
  27. 2021TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Trackingcited by 93
  28. 2021Opening up Open-World Trackingcited by 50
  29. 20211st Place Solution to ECCV-TAO-2020: Detect and Represent Any Object for Trackingcited by 17
  30. 2020SoDA: Multi-Object Tracking with Soft Data Associationcited by 12
  31. 2022Recent Advances in Embedding Methods for Multi-Object Tracking: A Surveycited by 8

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