A Fast Algorithm for Moving Objects Detection Based on Model Switching
A new method is proposed to improve background modeling speed. First, the pixels in current frame are classified into two classes according to average background to reduce the computing load. Second, different models for instance kernel or GMM based algorithm are used necessarily to deal with dead lock of scene. Third, a kernel density estimation based on neighbor correlation is used to decrease the false positives. Last, the two algorithm detection results are fused to detect moving object by the label of pixel. In this paper, a novel description of correlation about the pixel with its around pixels and a strategy of background modeling are proposed. Experimental results of outdoor complex scene demonstrate that the new algorithm is robustness to noise and good for real-time moving object detection.
Chunhui Zhao Wei Liu Yi Wang Yongmei Cheng Hongcai Zhang
College of Automation, Northwestern Polytechnical University, Xian 710072
国际会议
2008 International Conference on Audio，Language and Image Processing(2008国际声音、语言、图像过程大会)
镇江
英文
143-146
2008-07-07（万方平台首次上网日期，不代表论文的发表时间）