Adaptive background updating algorithm for traffic congestion detection based on Kalman filtering and inter-frame centroid distanc
As current video based vehicle detection algorithms can not detect the traffic congestion accurately,this paper presents a new adaptive background updating algorithm based on Kalman filtering and inter-frame centroid distance.Firstly,a Gauss mixture background model is set up to extract the moving vehicles.Then,with Kalman filtering method,the moving vehicles are tracked to identify their motion states.This method predicts the centroid position of the next frame vehicles.The Euclidean distance of the centroids of the adjacent frames vehicles are counted and the appropriate threshold is set up to realize the identification and the mark of stationary vehicles in the video.This improved background updating algorithms can better judge the traffic congestion,and it lays a foundation for improving the accuracy rate of the detection of traffic flow.The proposed algorithm has been tested for multiple traffic videos.The results show that the algorithm is of good real-time ability,environmental adaptability and accuracy.
Gaussian mixture model Kalman filtering method traffic congestion Euclidean distance
Zhang Ping Luo Qian Zhou Siyang
Department of Information and Communication Engineering Beijing Information Science & Technology University Beijing, China
国际会议
重庆
英文
891-895
2015-12-19（万方平台首次上网日期，不代表论文的发表时间）