Vision-Based Moving Objects Detection with Background Modeling

Video detection has become an efficient technique support for collecting parameters of urban traffic. Detection of moving objects with background model in complex environment is developed in this paper. 1) In order to obtain moving objects from the video sequence efficiently, a background initialization algorithm based on clustering classifier is presented, all stable non-overlapping intervals in the temporal training sequence of each pixel are located as possible backgrounds by slip window; then the background interval is obtained from the classified data set of possible backgrounds by unsupervised clustering. 2) According to spatial-temporal property of pixels, the paper also presents mixture Gaussian background update algorithm based on object-level with moving segmentation. The method can get over the effect of objectpsilas long-term stop. The proposed approach is validated under real traffic scenes. Experimental results show that moving objects detection is robust and adaptive, can be well applied in real-world.