Human action recognition using star skeleton
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
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Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Comparison of Classifiers for Human Activity Recognition
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Fuzzy qualitative human motion analysis
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Computer Vision and Image Understanding
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MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
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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 new approach to automatically recognize complex human activities embedded in video sequences acquired with a large scale view in order to monitoring wide area (car parking, archeological site. etc) with a single static camera. The recognition process is performed in two steps: at first the human body posture isestimated frame by frame and then the temporal sequences of the detected postures are statistically modeled. Body postures are estimated starting from the binary shapes associatedto humans, selecting as features the horizontal and vertical histograms and supplying them as input to an unsupervised clustering algorithm. The Manhattan distance is used for both clusters building and run-time classification. Statistical modeling of the detected postures is performed by Discrete HiddenMarkov Models. The system has been tested on image sequences acquired in an outdoor archaeological site. Four kinds of activities have been automatically classified with high percentage of right decisions.