A machine learning approach for human posture detection in domotics applications

This paper describes an approach for human postureclassification that has been devised for indoor surveillance in domotic applications. The approach was initially inspired to a previous works of Haritaoglou et al. [2] that uses histogram projections to classify people’s posture. We modify and improve the generality of the approach by adding a machine learning phase in order to generate probability maps. A statistic classifier has then defined that compares the probability maps and the histogram profiles extracted from each moving people. The approach results to be very robust if the initial constraints are satisfied and exhibits a very lowcomputational time so that it can be used to process livevideos with standard platforms.