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

Wei Song, Yifei Tian, Simon Fong, Kyungeun Cho, Wei Wang, Weiqiang Zhang
2016 Sustainability  
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
more » ... op 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. controlling module achieves an auto-controlling approach for energy management using smart meters. Meanwhile, the smart meters enable remote operation of switching and setting values through cloud computing and controlling. In the power management module, the power socket of an appliance is linked with a smart meter, whose controlling signals are able to control the connected appliance, such as changing temperature of air condition, turning on or turning off lights, heating water, and other activities. In such a sustainable energy management system, the real-time foreground objects detection in the monitoring module is an important task for smart controlling [3] . For example, in a garage, when a moving vehicle appears, the nearby lightings need to be turned on; if no foreground object exists, the lightings should be turned off so as to save electricity. To provide an effective environment monitoring approach, this paper aims to study a real-time and accurate foreground object detection algorithm for sustainable energy management. Meanwhile, fast and accurate foreground segmentation for video surveillance is a challenging task in image processing and computer vision [4] . This approach is also necessary for many multimedia applications, such as virtual reality, human-machine interaction, and mixed reality [5] . Sustainability 2016, 8, 916 2 of 20 smart meters. Meanwhile, the smart meters enable remote operation of switching and setting values through cloud computing and controlling. In the power management module, the power socket of an appliance is linked with a smart meter, whose controlling signals are able to control the connected appliance, such as changing temperature of air condition, turning on or turning off lights, heating water, and other activities. In such a sustainable energy management system, the real-time foreground objects detection in the monitoring module is an important task for smart controlling [3] . For example, in a garage, when a moving vehicle appears, the nearby lightings need to be turned on; if no foreground object exists, the lightings should be turned off so as to save electricity. To provide an effective environment monitoring approach, this paper aims to study a real-time and accurate foreground object detection algorithm for sustainable energy management. Meanwhile, fast and accurate foreground segmentation for video surveillance is a challenging task in image processing and computer vision [4] . This approach is also necessary for many multimedia applications, such as virtual reality, humanmachine interaction, and mixed reality [5] .
doi:10.3390/su8100916 fatcat:xxrdaewqljeqfbwogrftqfatey