Applying 3D human model in a posture recognition system
Pattern Recognition Letters - Special issue on vision for crime detection and prevention
Robust, low-cost, non-intrusive sensing and recognition of seated postures
Proceedings of the 20th annual ACM symposium on User interface software and technology
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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 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 low computational time so that it can be used to process live videos with standard platforms.