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Co-Design Dedicated System for Efficient Object Tracking Using Swarm Intelligence-Oriented Search Strategies.

Nadia Nedjah1, Alexandre V Cardoso1, Yuri M Tavares1

  • 1Department of Electronics Engineering and Telecommunications, State University of Rio de Janeiro, Rio de Janeiro 20.550-900, Brazil.

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Summary

This study introduces a hardware coprocessor for template matching in images, accelerating target detection. Particle Swarm Optimization (PSO) achieved 30 frames per second processing with a 95% success rate.

Keywords:
image cross-correlationobject trackingswarm intelligencetemplate matching

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Area of Science:

  • Computer Engineering
  • Image Processing
  • Artificial Intelligence

Background:

  • Template matching is crucial for pattern recognition in images.
  • Normalized cross-correlation is computationally intensive, especially for large images.
  • Hardware acceleration and swarm intelligence can improve template matching efficiency.

Purpose of the Study:

  • To design a hardware coprocessor for efficient template matching.
  • To evaluate swarm intelligence techniques for accelerating the target search process.
  • To achieve real-time video processing (30 frames per second) with high accuracy.

Main Methods:

  • A co-design system integrating a hardware coprocessor for normalized cross-correlation calculation.
  • Implementation and evaluation of six swarm intelligence algorithms: PSO, ABC, FFA, CS, FWA, EHO, and BFOA.
  • Comparison of processing time, iteration count, and success rate.

Main Results:

  • The co-design approach enabled processing video images at 30 frames per second.
  • PSO, ABC, FFA, and CS achieved >80% accuracy, meeting the timing requirements.
  • PSO demonstrated superior performance with 16.22 ms processing time and 95% success rate.

Conclusions:

  • A hardware-accelerated co-design system can effectively perform template matching for real-time video analysis.
  • Particle Swarm Optimization is the most effective swarm intelligence technique for this application.
  • The system achieves a balance between processing speed and target detection accuracy.