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
Cast Shadow Removing in Foreground Segmentation
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Efficient compression and network adaptive video coding for distributed video surveillance
Multimedia Tools and Applications
OS-Guard: on-site signature based framework for multimedia surveillance data management
Multimedia Tools and Applications
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This work deals with the automatic recognition of human activities embedded in video sequences acquired in an archeological site. The recognition process is performed in two steps: first of all the body posture of segmented human blobs is estimated frame by frame and then, 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.