关键词: 背景分类, 背景更正机制, 权重机制, 运动目标检测

Abstract: Aiming at surveillance videos which have low light, low contrast and low resolution, this paper proposes a moving target detection algorithm based on background classification. First, the video image hue H and brightness V of HSV in the first frames are used to initialize the background characteristics in order to build two kinds of background model classes including hue and brightness characteristics, namely the original background class that is obtained in the initialization background and the background fluctuating class influenced by lighting or other factors around the original background. These two models are used to detect foreground and to update background model. To improve the accuracy of the foreground detection, the background correction mechanism and weighting mechanism are utilized to correct the background model, so the number of samples in the background changes according to the actual situation in the background in order to improve the efficiency of the foreground segmentation. Experimental results show that the algorithm can obtain complete and clear foreground image and fast processing speed in different scenarios. This algorithm is simple and practical, and has better robustness for noise.

Key words: background classification, background correction mechanism, weight mechanism, moving target detection