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Background segmentation in difficult weather conditions.

Lukasz Karbowiak1, Janusz Bobulski1

  • 1Department of Computer Science, Czestochowa University of Technology, Czestochowa, Poland.

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|May 31, 2022
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Summary
This summary is machine-generated.

This study compares background segmentation algorithms under severe weather like snow and rain. Results reveal significant differences in how algorithms detect details and handle noise in challenging conditions.

Keywords:
Computer visionImage processingSegmentation

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Area of Science:

  • Computer Vision
  • Image Processing

Background:

  • Background segmentation is crucial for motion detection and object tracking.
  • Existing algorithms face challenges in adverse weather conditions.
  • Severe weather impacts image analysis and requires robust solutions.

Purpose of the Study:

  • To evaluate and compare the performance of background segmentation algorithms.
  • To assess algorithm efficacy under specific severe weather conditions: falling snow, rain, and sunny/windy days.
  • To identify algorithm limitations and strengths in adverse environmental scenarios.

Main Methods:

  • Implemented and tested several established background segmentation algorithms.
  • Utilized video frames captured by a Raspberry Pi camera under diverse weather conditions.
  • Included various objects in test frames: cars, bicycles, motorcycles, people, and trees.

Main Results:

  • Preliminary findings indicate notable variations in algorithm performance.
  • Differences observed in the precision of detail detection across algorithms.
  • Algorithm-specific noise levels were identified under tested weather conditions.

Conclusions:

  • Algorithm performance in background segmentation varies significantly under severe weather.
  • Severe weather conditions pose distinct challenges for detail detection and noise management.
  • Further research is needed to develop more resilient background segmentation techniques for adverse environments.