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 sfip 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 pixeis, the paper also presents Mixture Gaussian background update algorithm based on object-level with moving segmentation. The method can get over the effect of objects 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.
intelligence transportation system video detection background model mizture Gaussian
Hongyu Hu Zhihui Li Zhaowei Qu Dianhai Wang
College of Transportation Jilin University Changchun,China
国际会议
长沙
英文
1380-1383
2009-04-11（万方平台首次上网日期，不代表论文的发表时间）