GPU-Accelerated Foreground Segmentation and Labeling for Real-Time Video Surveillance
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. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
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.
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(10):916.Chicago/Turabian Style
Song, Wei; Tian, Yifei; Fong, Simon; Cho, Kyungeun; Wang, Wei; Zhang, Weiqiang. 2016. "GPU-Accelerated Foreground Segmentation and Labeling for Real-Time Video Surveillance." Sustainability 8, no. 10: 916.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.