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GPU-Accelerated Foreground Segmentation and Labeling for Real-Time Video Surveillance

Author

Listed:
  • Wei Song

    (Department of Digital Media Technology, North China University of Technology, Beijing 100144, China
    Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, North China University of Technology, Beijing 100144, China)

  • Yifei Tian

    (Department of Digital Media Technology, North China University of Technology, Beijing 100144, China)

  • Simon Fong

    (Department of Computer and Information Science, University of Macau, Macau, China)

  • Kyungeun Cho

    (Department of Multimedia Engineering, Dongguk University, Seoul 04620, Korea)

  • Wei Wang

    (Guangdong Electronic Industry Institute, Dongguan 523808, China)

  • Weiqiang Zhang

    (Department of Multimedia Engineering, Dongguk University, Seoul 04620, Korea)

Abstract

Real-time and accurate background modeling is an important researching topic in the fields of remote monitoring and video surveillance. Meanwhile, effective foreground detection is a preliminary requirement and decision-making basis for sustainable energy management, especially in smart meters. The environment monitoring results provide a decision-making basis for energy-saving strategies. For real-time moving object detection in video, this paper applies a parallel computing technology to develop a feedback foreground–background segmentation method and a parallel connected component labeling (PCCL) algorithm. In the background modeling method, pixel-wise color histograms in graphics processing unit (GPU) memory is generated from sequential images. If a pixel color in the current image does not locate around the peaks of its histogram, it is segmented as a foreground pixel. From the foreground segmentation results, a PCCL algorithm is proposed to cluster the foreground pixels into several groups in order to distinguish separate blobs. Because the noisy spot and sparkle in the foreground segmentation results always contain a small quantity of pixels, the small blobs are removed as noise in order to refine the segmentation results. The proposed GPU-based image processing algorithms are implemented using the compute unified device architecture (CUDA) toolkit. The testing results show a significant enhancement in both speed and accuracy.

Suggested Citation

  • Wei Song & Yifei Tian & Simon Fong & Kyungeun Cho & Wei Wang & Weiqiang Zhang, 2016. "GPU-Accelerated Foreground Segmentation and Labeling for Real-Time Video Surveillance," Sustainability, MDPI, vol. 8(10), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:10:p:916-:d:79410
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    References listed on IDEAS

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    1. Carroll, James & Lyons, Seán & Denny, Eleanor, 2014. "Reducing household electricity demand through smart metering: The role of improved information about energy saving," Energy Economics, Elsevier, vol. 45(C), pages 234-243.
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    Cited by:

    1. Eun-Seok Lee & Byeong-Seok Shin, 2019. "Hardware-Based Adaptive Terrain Mesh Using Temporal Coherence for Real-Time Landscape Visualization," Sustainability, MDPI, vol. 11(7), pages 1-18, April.
    2. Jong Hyuk Park & Han-Chieh Chao, 2017. "Advanced IT-Based Future Sustainable Computing," Sustainability, MDPI, vol. 9(5), pages 1-4, May.

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