Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training Hidden Markov Models with Multiple Observations-A Combinatorial Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning variable-length Markov models of behavior
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Human Activity Recognition Using Multidimensional Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision-Based Gesture Recognition: A Review
GW '99 Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction
Learning dynamics for exemplar-based gesture recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Logical Hierarchical Hidden Markov Models for Modeling User Activities
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Viewpoint insensitive posture representation for action recognition
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
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The problem of automatic recognition of human activities is among the most important and challenging open areas of research in Computer Vision. This paper presents a methodology to automatically recognize the human activities embedded in video sequences acquired in outdoor environments by a single large view camera. The activity recognition process is performed in three steps: first of all the binary shape of moving people are segmented, then the human body posture is estimated frame by frame and finally, for each activity to be recognized, a temporal model of the detected postures is generated by Discrete Hidden Markov Models. The system has been tested on image sequences acquired in a real archaeological site meanwhile actors perform both legal and illegal actions. Four kinds of activities have been automatically classified with high percentage of correct decisions. Time performance tests are very encouraging for using the proposed method in real time applications.