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Moving Object Detection Using Scanning Camera on a High-Precision Intelligent Holder.

Shuoyang Chen1, Tingfa Xu2,3, Daqun Li4

  • 1School of Optoelectronics, Image Engineering & Video Technology Lab, Beijing Institute of Technology, Beijing 100081, China. 2120140517@bit.edu.cn.

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|October 25, 2016
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Summary
This summary is machine-generated.

This study introduces a novel algorithm for moving object detection in complex surveillance backgrounds, improving upon traditional methods. The enhanced approach effectively handles illumination variations and accurately identifies objects using edge detection and Local Binary Pattern analysis.

Keywords:
background modelingintelligent visual surveillancemoving object detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Surveillance Systems

Background:

  • Traditional methods like frame difference and optical flow struggle with complex backgrounds in intelligent visual surveillance.
  • Illumination variations pose a significant challenge for accurate moving object detection.

Purpose of the Study:

  • To develop a robust algorithm for moving object detection in intelligent visual surveillance systems, particularly in complex background scenarios.
  • To enhance resistance to illumination variations and improve segmentation accuracy.

Main Methods:

  • Utilized edge detection to generate an edge difference image, enhancing robustness against illumination changes.
  • Employed a multi-block temporal-analyzing Local Binary Pattern (LBP) algorithm for improved background segmentation.
  • Integrated connected component analysis for precise object localization.

Main Results:

  • The modified algorithm demonstrates improved performance in complex background scenarios compared to traditional methods.
  • Edge detection effectively mitigated issues caused by illumination variations.
  • The multi-block temporal-analyzing LBP algorithm provided accurate segmentation of moving objects.

Conclusions:

  • The proposed method offers a more reliable solution for moving object detection in challenging surveillance environments.
  • The combination of edge detection, LBP, and connected components provides a robust framework for intelligent visual surveillance.
  • A dedicated hardware platform featuring Digital Signal Processor (DSP) and Field Programmable Gate Array (FPGA) was developed to support the algorithm.