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Shadow Elimination Algorithm Using Color and Texture Features.

Minghu Wu1, Rui Chen2, Ying Tong2

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Summary
This summary is machine-generated.

This study presents an improved algorithm for shadow detection and removal in urban surveillance videos. The method enhances target detection by effectively eliminating shadow interference using HSV color space and texture features.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Shadows in real-world images pose challenges for accurate target detection.
  • Existing methods often struggle with shadow interference in urban surveillance.

Purpose of the Study:

  • To develop an improved algorithm for shadow detection and removal in urban video surveillance.
  • To enhance the accuracy of moving target detection by eliminating shadow interference.

Main Methods:

  • Foreground detection via background subtraction.
  • Shadow detection utilizing the HSV color space.
  • Texture feature extraction using local variance and the OTSU method.
  • Shadow removal based on HSV characteristics and texture features.
  • Algorithm integration into a C/S framework with HTML5 WebSocket protocol.

Main Results:

  • The proposed algorithm effectively detects and removes shadows.
  • Shadow interference is successfully eliminated for subsequent moving target processing.
  • The algorithm demonstrates efficiency and robustness across various scenes.
  • Experimental and operational results validate the algorithm's performance.

Conclusions:

  • The developed algorithm provides an efficient and robust solution for shadow detection and removal in urban surveillance.
  • This method significantly improves the reliability of target detection in the presence of shadows.
  • The integration into a C/S framework enables practical application in real-time surveillance systems.