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Pixel-Guided Association for Multi-Object Tracking.

Abhijeet Boragule1, Hyunsung Jang2, Namkoo Ha2

  • 1School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea.

Sensors (Basel, Switzerland)
|November 26, 2022
PubMed
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This study introduces a novel pixel-guided framework for unified multi-object tracking (MOT), improving trajectory linking. The approach achieves state-of-the-art performance on standard benchmarks.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Multi-Object Tracking (MOT) relies on accurate trajectory linking.
  • Current deep learning models often use fragmented solutions for appearance, motion, and association.
  • A unified approach is needed for efficient and effective MOT.

Purpose of the Study:

  • To introduce a unified, pixel-guided framework for joint detection and tracking in MOT.
  • To enhance the association of object trajectories by leveraging pixel-level information.
  • To improve overall MOT performance through a cohesive approach.

Main Methods:

  • Utilized up-sampled multi-scale features from consecutive frames with a transformer-decoder for object detection.
  • Employed per-pixel distributions to compute the association matrix based on object queries.
Keywords:
multi-object trackingobject detectiontransformer

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  • Introduced long-term appearance association on track features for robust track-to-detection similarity.
  • Integrated the similarity matrix with the Byte-Tracker algorithm.

    • Main Results:

    • Achieved state-of-the-art performance in Multi-Object Tracking.
    • Demonstrated significant tracking improvements on the MOT15 and MOT17 benchmarks.
    • The pixel-guided, unified framework effectively links object trajectories.

    Conclusions:

    • The proposed pixel-guided approach offers a unified solution for joint detection and tracking in MOT.
    • This method significantly enhances the accuracy of object trajectory association.
    • The framework represents a substantial advancement in MOT performance.