Parallel Algorithm for Moving Foreground Detection in Dynamic Background

Foreground detection in dynamic background has become a hot topic in video surveillance in recent years. In this paper we propose a new foreground detection approach based on GPU in dynamic background. With the proposed method, SIFT features are first extracted from two adjacent frames in video sequences, which can be utilized to compute the parameters of affine transform model and to solve global motion compensation. Then improving background subtraction approach with dynamic background updating module is adopted to detect foreground objects. GPU method is used to improve application performance. Combined with CUDA, three mainly algorithm modules, which are so called Global Motion Compensation Module, Updating Background Module and Foreground Detection Module, are improved. In this paper, GPU and CPU are used as a combined computing unit, which makes good use of strong parallel computing ability. The effectiveness of the method has been proved. Finally, the contrasting experiments on processing time show that the proposed algorithm based on GPU is better in speed.