Posture recognition in visual surveillance of archeological sites

The main aim of this work is to present a simple and reliable approach to the estimation of human body postures. The applicative context is the visual surveillance of an archaeological site. Motion detection and object recognition subsystems process image sequences coming from a still camera. Whenever a human is detected, his postures are characterized by the proposed pose estimation module. Then the results are fed to a HMM subsystem that identifies the current activity of the examined subject The proposed algorithm is based on an unsupervised clustering approach that makes the system substantially independent from any a-priori assumption about the possible output postures. The features selected for posture estimation are the horizontal and vertical histograms of binary shapes. A modified version of the Manhattan distance is used for both cluster identification and for run-time classification. After extensive experimental tests with different clustering schema, the BCLS algorithm (basic competitive learning scheme) has been selected. The proposed approach makes possible to change the number of classes, during the classification phase, without repeating the training step. Moreover it provides a measure of the reliability of its results. The proposed method has been verified on sequences acquired while typical illegal activities involved in stealing were simulated in a real archaeological site.