A foreground detection algorithm based on improved three-frame difference method and improved Gaussian mixed model

Min Shang; Shan Zeng; Liang Jiang

doi:10.1117/12.2524377

6 May 2019 A foreground detection algorithm based on improved three-frame difference method and improved Gaussian mixed model

Min Shang, Shan Zeng, Liang Jiang

Author Affiliations +

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110693T (2019) https://doi.org/10.1117/12.2524377
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

Abstract

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.

Citation Download Citation

Min Shang, Shan Zeng, and Liang Jiang "A foreground detection algorithm based on improved three-frame difference method and improved Gaussian mixed model", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110693T (6 May 2019); https://doi.org/10.1117/12.2524377

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