Real-time background subtraction based on GPGPU for high-resolution video surveillance
Sunhee Hwang, Youngjung Uh, Minsong Ki, Kwangyong Lim, Daeyong Park, Hyeran Byun · 2017
Demand for intelligent surveillance has been increasing, to automatically detect and prevent dangerous situations with surveillance cameras. Image analysis, the most essential element in intelligent surveillance system, has continuously developed and contributed to the improvement. To analyze surveillance videos, foreground segmentation is vital which require background modeling. This paper proposes background modeling method which is robust to illumination variation and shadow area. Also, the proposed method is applicable to high-resolution videos in real time with modification for GPU implementation. We validate our method on different types of dataset including our new benchmark dataset to analyze the result quantitatively and qualitatively. The execution time of proposed method is 228.2 FPS for High Definition videos with NVIDIA GTX660.