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Intelligent Object Tracking with an Automatic Image Zoom Algorithm for a Camera Sensing Surveillance System.

Shih-Chang Hsia1, Szu-Hong Wang1, Chung-Mao Wei1

  • 1Department of Electronic Engineering, National Yunlin University of Science and Technology, Douliu City, Yunlin County 64002, Taiwan.

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|November 26, 2022
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Summary
This summary is machine-generated.

This study introduces an advanced surveillance algorithm that automatically detects, tracks, and zooms on targets, enhancing resolution for distant objects and conserving disk space by recording only when targets are present.

Keywords:
camera sensingmotion detectionobject trackingsurveillance system

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Area of Science:

  • Computer Vision
  • Robotics
  • Surveillance Technology

Background:

  • Current surveillance systems with fixed cameras suffer from low resolution for distant objects and inefficient data storage.
  • Limitations include restricted frame ranges and significant wasted disk space due to continuous recording.

Purpose of the Study:

  • To develop a novel algorithm for automatic target detection, tracking, and zooming using a 360° pan-tilt-zoom camera.
  • To improve image resolution for small or distant objects and optimize data storage efficiency.

Main Methods:

  • An adaptive object segmentation algorithm for efficient detection of moving object shapes.
  • A predictive tracking algorithm controlling camera movement to maintain optimal image quality and adapt to varying target speeds.
  • Separate color-coding for multiple targets to enable individual tracking.

Main Results:

  • The system effectively improves resolution for distant or small objects by combining tracking and zoom capabilities.
  • Disk space is conserved as recording is deactivated when no valid targets are detected.
  • The algorithm successfully locks onto human heads in tests, though simultaneous tracking and zooming occasionally caused tracking failures.

Conclusions:

  • The proposed algorithm offers a significant improvement over traditional surveillance systems by enhancing object resolution and data efficiency.
  • Integration with facial recognition could further boost recognition accuracy.
  • Potential for improved surveillance performance in various applications.