A machine learning approach for human posture detection in domotics applications

Abstract

This paper describes an approach for human posture classification that has been devised for indoor surveillance in domotic applications. The approach was initially inspired to a previous work of Haritaoglou et al. (1998) 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 of the moving people. The approach is very robust if the initial constraints are satisfied and exhibits a very low computational time so that it can be used to process live videos with standard platforms.