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

Wei Song, Yifei Tian, Simon Fong, Kyungeun Cho, Wei Wang and Weiqiang Zhang
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Wei Song: Department of Digital Media Technology, 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

Sustainability, 2016, vol. 8, issue 10, 1-20

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.

Keywords: feedback background modeling; connected component labeling; parallel computation; video surveillance; sustainable energy management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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