An improved motion detection method for real-time surveillance
N. Lu, Jihong Wang, Qin Wu, Li Yang · 2008
Real-time detection of moving objects is very important for video surveillance. In this paper, a novel real time motion detection algorithm is proposed. The algorit hm integrates the temporal differencing method, optical flow method, double background filtering (DBF) method and morphological processing methods to achieve better performance. The temporal differencing is used to detect initial coarse motio n areas for the optical flow calculation to achieve real-time and a ccurate object motion detection. The DBF method is used to obtain and keep a stable background image to cope with variations on environmental changing conditions and is used to el iminate the background interference information and separate the moving object from it. The morphological processing methods are adopted and combined with the DBF to get improved results. The most attractive advantage of this algorithm is that the algorithm does not need to learn the background model from hundreds of images and can handle quick image variations without prior knowledge about the object size and shape. The algorithm has high capability of anti-interference and preserves high accurate rate detection at the same time. It also demands le ss computation time than other methods for the real-time surveilla nce. The effectiveness of the proposed algorithm for motion detection is demonstrated in a simulation environment and the evaluation results are reported in this paper.