A Method of FAM for Pedestrian Behaviour Classification
In this paper, we present the classification of motion, and make a conclusions under specific circumstances. In order to indicate a pedestrians movements, a complex number notation based on centroid is proposed. And according to the different sorts of movements, a set of standard image contours are made. Different behaviour matrices based on spatio-temporal are acquired through Hidden Markov Models (HMM). A Procrustes Mean Shape Distance (PMSD) analysis method is presented in order to get the degree to which two contours are resembled. AS a final result, a learning Fuzzy Associative Memory (FAM) is proposed to infer behavior classification of a walker,and an evaluation of six kinds of gaits involving walking, side walking, jumping, crouching, uphill and downhill is given with a 62.5％ recognition rate achieved. Experiment results shows the system computational cost has been effectively reduced ,the robustness of method has been validated by experiments.
number notation spatio-temporal Hidden Markov Models Learning Fuzzy Associative Memory(FAM)
Chunfang Zhang Hui Sun Hongxia Zhou Ruitai Li
Mathematics and Information Department, Hebei Normal University No.113 Yuhuadong Road, Shijiazhuang, Hebei Province, 050016, China
国际会议
Second International Symposium on Electronic Commerce and Security(第二届电子商务与安全国际研究大会)(ISECS 2009)
南昌
英文
806-810
2009-05-22（万方平台首次上网日期，不代表论文的发表时间）