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Sustainability 2016, 8(10), 916; doi:10.3390/su8100916

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

1
Department of Digital Media Technology, North China University of Technology, Beijing 100144, China
2
Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, North China University of Technology, Beijing 100144, China
3
Department of Computer and Information Science, University of Macau, Macau, China
4
Department of Multimedia Engineering, Dongguk University, Seoul 04620, Korea
5
Guangdong Electronic Industry Institute, Dongguan 523808, China
*
Authors to whom correspondence should be addressed.
Academic Editor: James Park
Received: 30 May 2016 / Revised: 22 August 2016 / Accepted: 22 August 2016 / Published: 29 September 2016
(This article belongs to the Special Issue Advanced IT based Future Sustainable Computing)
View Full-Text   |   Download PDF [5890 KB, uploaded 29 September 2016]   |  

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. View Full-Text
Keywords: feedback background modeling; connected component labeling; parallel computation; video surveillance; sustainable energy management feedback background modeling; connected component labeling; parallel computation; video surveillance; sustainable energy management
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MDPI and ACS Style

Song, W.; Tian, Y.; Fong, S.; Cho, K.; Wang, W.; Zhang, W. GPU-Accelerated Foreground Segmentation and Labeling for Real-Time Video Surveillance. Sustainability 2016, 8, 916.

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