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A coded aperture compressive imaging array and its visual detection and tracking algorithms for surveillance systems.

Jing Chen1, Yongtian Wang, Hanxiao Wu

  • 1Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China. chen74jing29@bit.edu.cn

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Summary
This summary is machine-generated.

This study introduces a compressive imaging system for wide-area video surveillance, enabling efficient motion target detection and tracking using compressed sensing images. Random binary phase masks offer superior detection, while Gaussian and Toeplitz masks provide higher resolution reconstructions.

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Area of Science:

  • Computer Vision
  • Signal Processing
  • Surveillance Technology

Background:

  • Wide-area video surveillance systems require efficient methods for detecting and tracking motion targets.
  • Traditional imaging systems face challenges with high data rates and storage requirements for surveillance applications.

Purpose of the Study:

  • To propose and evaluate a compressive imaging system for wide-area video surveillance.
  • To develop motion target detection and tracking algorithms that operate directly on compressive sensing images.
  • To compare the performance of different coded masks in terms of detection accuracy and image reconstruction quality.

Main Methods:

  • A parallel coded aperture compressive imaging system was designed to reduce mask complexity and storage needs.
  • Random Gaussian, Toeplitz, and binary phase coded masks were used to acquire compressive sensing images.
  • Motion detection algorithms utilizing mixture of Gaussian distribution for background modeling were developed.
  • Sparse representation with an l(1) optimization algorithm was employed for target feature extraction.
  • A real-time tracking algorithm was implemented and compared against a standard l(1) tracker.

Main Results:

  • Low-dimensional compressed imaging representations are sufficient for spatial motion target determination.
  • Random binary phase masks yielded superior motion detection results compared to Gaussian and Toeplitz masks.
  • Random Gaussian and Toeplitz masks enabled high-resolution image reconstruction.
  • The proposed tracking algorithm achieved real-time performance, up to 10 times faster than the unoptimized l(1) tracker.

Conclusions:

  • Compressive imaging offers a viable solution for efficient wide-area video surveillance.
  • The choice of coded mask impacts a trade-off between detection performance and image resolution.
  • The developed algorithms provide effective and efficient motion target detection and tracking in compressed domains.