mlsanityData to 7 Oct 2026

CAMOT: Camera Angle-aware Multi-Object Tracking

arXiv 2409.17533 ↗First posted 26 Sep 2024cs.CVcited by 4

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Abstract (arXiv)

This paper proposes CAMOT, a simple camera angle estimator for multi-object tracking to tackle two problems: 1) occlusion and 2) inaccurate distance estimation in the depth direction. Under the assumption that multiple objects are located on a flat plane in each video frame, CAMOT estimates the camera angle using object detection. In addition, it gives the depth of each object, enabling pseudo-3D MOT. We evaluated its performance by adding it to various 2D MOT methods on the MOT17 and MOT20 datasets and confirmed its effectiveness. Applying CAMOT to ByteTrack, we obtained 63.8% HOTA, 80.6% MOTA, and 78.5% IDF1 in MOT17, which are state-of-the-art results. Its computational cost is significantly lower than the existing deep-learning-based depth estimators for tracking.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 53 of its 65 references that are in the map. First posted on arXiv 26 Sep 2024.

Builds on

53 of 65 references.

References · 53

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38 more
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50 most cited of 53 All 53 (JSON)

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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025DepTR-MOT: Unveiling the Potential of Depth-Informed Trajectory Refinement for Multi-Object Trackingcited by 1
  2. 2025EPIPTrack: Rethinking Prompt Modeling with Explicit and Implicit Prompts for Multi-Object Trackingcited by 1
  3. 2025From Detection to Association: Learning Discriminative Object Embeddings for Multi-Object Trackingcited by 1
  4. 2025GRASPTrack: Geometry-Reasoned Association via Segmentation and Projection for Multi-Object Trackingcited by 0