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Object Relocation Visual Tracking Based on Histogram Filter and Siamese Network in Intelligent Transportation.

Jianlong Zhang1, Yifan Liu1, Qiao Li1

  • 1School of Electronic Engineering, Xidian University, Xi'an 710071, China.

Sensors (Basel, Switzerland)
|November 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel approach for autonomous driving target tracking, enhancing robustness against object deformation and viewpoint changes. The method improves tracking accuracy and object re-acquisition capabilities, outperforming existing state-of-the-art techniques.

Keywords:
Siamese networkdynamic template setmatch filtersingle object tracking

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Autonomous driving relies on robust target detection and tracking for localization and path planning.
  • Siamese network trackers simplify visual tracking but struggle with feature representation, object variations, and tracking failures.

Purpose of the Study:

  • To enhance the robustness and adaptability of Siamese network-based trackers for autonomous driving applications.
  • To address limitations in feature representation, object variation adaptation, and object re-acquisition after tracking failure.

Main Methods:

  • A novel match filter arbiter using Euclidean distance histograms to detect tracker failure.
  • Integration of the Hopcroft-Karp algorithm for dynamic template selection and backtracking.
  • Object re-localization via Gradient Magnitude Similarity Deviation comparison.

Main Results:

  • The proposed method demonstrates superior performance on benchmark datasets including OTB100, VOT2018, GOT-10k, and LaSOT.
  • Improved tracking accuracy and robustness against distractors like deformation and similar objects.
  • Effective object re-acquisition after tracking loss.

Conclusions:

  • The developed tracker significantly enhances autonomous driving perception systems.
  • The novel methods for failure detection and re-acquisition overcome key limitations of existing trackers.
  • The approach offers a more reliable solution for real-world intelligent transportation scenarios.