Applied Research in Fuzzy Technology: Results of the Laboratory for International Fuzzy Engineering (Life)
Real-time Human Motion Analysis by Image Skeletonization
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Detecting Irregularities in Images and in Video
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Hidden Markov models in biological sequence analysis
IBM Journal of Research and Development
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Abnormal behavior detecting is one of the hottest but most difficult subjects in Monitoring System. It is hard to define "abnormal" in different scenarios. In this paper firstly the classification of motion is conducted, and then conclusions are made under specific circumstances. In order to indicate a pedestrian's 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 behavior matrices based on spatio-temporal are acquired through Hidden Markov Models (HMM). A Procrustes shape analysis method is presented in order to get the similarity degree of two contours. Finally Fuzzy Associative Memory (FAM) is proposed to infer behavior classification of a walker. Thus anomalous pedestrians can be detected in the given condition. FAM can detect irregularities and implement initiative analysis of body behavior.