Real-Time Pedestrian Detection Based on Improved Gaussian Mixture Model
Applying image processing technologies to pedestrian detection has been a hot research topic in Intelligent Transportation Systems (ITS). However, the existing video-based algorithms to extract background image may suffer their inefficiency in detecting slow or static pedestrians. To fill the gap, an improved Gaussian Mixture Model (GMM) for pedestrian detection is proposed in this paper. Three novel components have been incorporate into the traditional model Firstly, the phase of graph segmentation is added before conventional parameters updating. Secondly, a mergence time adjustment scheme is employed to prevent foreground from merging into background. Thirdly, the notion of average weight is introduced as a secondary judgment criterion of foreground segmentation. To show the performance of the proposed method, this algorithm is applied into the real videos for pedestrian detection. The results show the accuracy and adaptability of this proposed method are over standard GMM.
ITS pedestrian detection background subtraction Gaussian Mixture Model
Juan Li Chunfu Shao Wangtu Xu Chunjiao Dong
School of Traffic and Transportation Beijing Jiaotong University
国际会议
张家界
英文
269-272
2009-04-11（万方平台首次上网日期，不代表论文的发表时间）