Posture classification in a multi-camera indoor environment
Posture classification is a key process for analyzing the people's behaviour. Computer vision techniques can be helpful in automating this process, but cluttered environments and consequent occlusions make this task often difficult. Different views provided by multiple cameras can be exploited to solve occlusions by warping known object appearance into the occluded view. To this aim, this paper describes an approach to posture classification based on projection histograms, reinforced by HMM for assuring temporal coherence of the posture. The single camera posture classification is then exploited in the multi-camera system to solve the cases in which the occlusions make the classification impossible. Experimental results of the classification from both the single camera and the multi-camera system are provided.