UM
GPU-accelerated foreground segmentation and labeling for real-time video surveillance
Song W.1; Tian Y.1; Fong S.3; Cho K.4; Wang W.2; Zhang W.4
2016-09-29
Source PublicationSustainability (Switzerland)
ISSN20711050
Volume8Issue:10
AbstractReal-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.
KeywordConnected component labeling Feedback background modeling Parallel computation Sustainable energy management Video surveillance
DOI10.3390/su8100916
URLView the original
Language英語
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Cited Times [WOS]:8   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.North China University of Technology
2.Guangdong Electronics Industry Institute
3.Universidade de Macau
4.Dongguk University, Seoul
Recommended Citation
GB/T 7714
Song W.,Tian Y.,Fong S.,et al. GPU-accelerated foreground segmentation and labeling for real-time video surveillance[J]. Sustainability (Switzerland),2016,8(10).
APA Song W.,Tian Y.,Fong S.,Cho K.,Wang W.,&Zhang W..(2016).GPU-accelerated foreground segmentation and labeling for real-time video surveillance.Sustainability (Switzerland),8(10).
MLA Song W.,et al."GPU-accelerated foreground segmentation and labeling for real-time video surveillance".Sustainability (Switzerland) 8.10(2016).
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