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Target Detection Based on Improved Hausdorff Distance Matching Algorithm for Millimeter-Wave Radar and Video Fusion.

Dongpo Xu1, Yunqing Liu1, Qian Wang1

  • 1School of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China.

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Summary

This study introduces a novel method to fuse camera and millimetre-wave radar data for improved vehicle detection in intelligent transportation systems (ITS). The approach enhances multi-target tracking accuracy, even with occlusions and environmental interference.

Keywords:
intelligent transportation systemsmillimeter-wave radarspatio-temporal alignmenttarget matchingvideo

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Area of Science:

  • Computer Vision
  • Sensor Fusion
  • Intelligent Transportation Systems

Background:

  • Intelligent transportation systems (ITS) rely heavily on video surveillance for traffic monitoring.
  • Challenges in vehicle detection include target variety, occlusion, and environmental interference, limiting single-sensor effectiveness.

Purpose of the Study:

  • To develop an improved data matching method for fusing camera and millimetre-wave radar data.
  • To enhance multi-target data alignment and correlation in spatial dimensions for ITS.
  • To address poor recognition alignment caused by occlusion and environmental disturbances.

Main Methods:

  • Spatio-temporal alignment of camera and millimetre-wave radar sensors.
  • Calibration of radar and pixel coordinate systems using Lagrangian interpolation.
  • Application of an improved Hausdorff distance matching algorithm for data similarity calculation and target matching.

Main Results:

  • Successful fusion of video and millimetre-wave radar data for improved vehicle detection.
  • Accurate spatio-temporal alignment and calibration between sensors.
  • Effective delineation of regions of interest (ROI) for target vehicles using enhanced data matching.

Conclusions:

  • The proposed data fusion method significantly improves vehicle detection accuracy in complex ITS environments.
  • Sensor fusion effectively mitigates issues related to occlusion and environmental interference.
  • The approach provides a robust solution for multi-target vehicle tracking and recognition in intelligent transportation systems.