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Multi-Object Tracking by Hierarchical Visual Representations

arXiv 2402.15895 ↗First posted 24 Feb 2024cs.CVcited by 0

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Abstract (arXiv)

We propose a new visual hierarchical representation paradigm for multi-object tracking. It is more effective to discriminate between objects by attending to objects' compositional visual regions and contrasting with the background contextual information instead of sticking to only the semantic visual cue such as bounding boxes. This compositional-semantic-contextual hierarchy is flexible to be integrated in different appearance-based multi-object tracking methods. We also propose an attention-based visual feature module to fuse the hierarchical visual representations. The proposed method achieves state-of-the-art accuracy and time efficiency among query-based methods on multiple multi-object tracking benchmarks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 45 of its 56 references that are in the map. First posted on arXiv 24 Feb 2024.

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45 of 56 references.

References · 45

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33 more
  1. 2021TrackFormer: Multi-Object Tracking with Transformerscited by 515
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  8. 2022Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Trackingcited by 345
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  10. 2020Quasi-Dense Similarity Learning for Multiple Object Trackingcited by 220
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  16. 2020TAO: A Large-Scale Benchmark for Tracking Any Objectcited by 146
  17. 2023Deep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-Identificationcited by 130
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  25. 2023MotionTrack: Learning Robust Short-term and Long-term Motions for Multi-Object Trackingcited by 64
  26. 2022Body Part-Based Representation Learning for Occluded Person Re-Identificationcited by 63
  27. 2022Unifying Short and Long-Term Tracking with Graph Hierarchiescited by 62
  28. 2021RelationTrack: Relation-aware Multiple Object Tracking with Decoupled Representationcited by 52
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  33. 2017Robust Visual Tracking via Hierarchical Convolutional Featurescited by 4

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