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The problem of automatic recognition of human activity is one of the most important and challenging areas of research in computer vision because of the wide range of possible applications, for example surveillance, advanced human‐computer interactions, monitoring. This paper presents statistical computer vision approaches to automatically recognize different human activities.

The human activity recognition process has three steps: firstly human blobs are segmented by motion analysis; then the human body posture is estimated and, finally a temporal model of the detected posture series is generated by discrete hidden Markov models to identify the activity.

The system was tested on image sequences acquired in a real archaeological site while some people simulated both legal and illegal actions. Four kinds of activity were automatically classified with a high percentage of correct detections.

The proposed approach provides efficient solutions to some of the most common problems in the human activity recognition research field: high detailed image requirement, sequence alignment and intensive user interaction in the training phase. The main constraint of this framework is that the posture estimation approach is not completely view independent.

Results of time performance tests were very encouraging for the use of the proposed method in real time surveillance applications.

The proposed framework can work using low cost cameras with large view focal lenses. It does not need any a priori knowledge of the scene and no intensive user interaction is required in the early training phase.