Human body modelling for people localization and tracking from real image sequences

Abstract

Computer-assisted surveillance of complex environments is becoming more and more interesting, thanks to significant improvements in real-time signal processing. In the surveillance research field applied to public areas, crowd monitoring is very useful but presents particularly complex problems. Recognizing human bodies and tracking their movements in complex real scenes by using a sequence of images are among the most difficult tasks in computer vision. The main difficulties arise both from the high level of shape variability associated with moving human bodies, and from the complexity of environmental real scenes. A method for tracking human motion in 3D real scenes by means of Kalman filtering is presented It is based on the introduction of a suitable mathematical model for a human body moving in a 3D scene and interacting with the surrounding environment. The developed refined static and dynamic models allow the system to be accurate and robust. Due to its real-time functioning, accuracy and robustness, the method can be used in surveillance systems devoted to assure public safety.