A foreground detection algorithm based on improved three-frame difference method and improved Gaussian mixed model
Min Shang, Shan Zeng, Liang Jiang · 2019
In video foreground detection, the frame difference method bears a fast detection speed and strong timeliness. However, the detection foreground target is not complete enough, tending to result in voids and poor robustness. Although the Gaussian mixed model does well in detection, a ghost image is easily brought forth when it starts foreground target detection towards sudden motion. As far as these problems are concerned, a foreground detection algorithm based on improved Gaussian mixed model is proposed in this paper. The foreground region detected by the Gaussian mixed model is matched with the one detected by the improved three-frame difference method with the matched foreground reserved. Then the unmatched one is regarded as a 'ghost' region. The background model of the region is updated and the mean of the maximum weighted Gaussian model is replaced in an usage of the pixel of the corresponding area, thus breaking the obstacles in traditional method for detecting holes and 'ghosting' problems. The experimental results have shown that the proposed algorithm has better robustness and accuracy in different backgrounds, and the precision and recall excel that of traditional algorithms.