
Proceedings Paper
Three-level GPU accelerated Gaussian mixture model for background subtractionFormat | Member Price | Non-Member Price |
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Paper Abstract
Gaussian Mixture Model (GMM) for background subtraction (BGS) is widely used for detecting and tracking objects in
video sequences. Although the GMM can provide good results, low processing speed has become its bottleneck for realtime
applications. We propose a novel method to accelerate the GMM algorithm based on graphics processing unit
(GPU). As GPU excels at performing massively parallel operations, the novelty lies in how to adopt various optimization
strategies to fully exploit GPU's resources. The parallel design consists of three levels. On the basis of first-level
implementation, we employ techniques such as memory access coalescing and memory address saving to the secondlevel
optimization and the third-level modification, which reduces the time cost and increases the bandwidth greatly.
Experimental results demonstrate that the proposed method can yield performance gains of 145 frames per second (fps)
for VGA (640*480) video and 505 fps for QVGA (320*240) video which outperform their CPU counterparts by 24X and 23X speedup respectively. The resulted surveillance system can process five VGA videos simultaneously with strong robustness and high efficiency.
Paper Details
Date Published: 2 February 2012
PDF: 6 pages
Proc. SPIE 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II, 829514 (2 February 2012); doi: 10.1117/12.906385
Published in SPIE Proceedings Vol. 8295:
Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II
Karen O. Egiazarian; John Recker; Guijin Wang; Sos S. Agaian; Atanas P. Gotchev, Editor(s)
PDF: 6 pages
Proc. SPIE 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II, 829514 (2 February 2012); doi: 10.1117/12.906385
Show Author Affiliations
Xinggang Lin, Tsinghua Univ. (China)
Published in SPIE Proceedings Vol. 8295:
Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II
Karen O. Egiazarian; John Recker; Guijin Wang; Sos S. Agaian; Atanas P. Gotchev, Editor(s)
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