A system for simultaneous people tracking and posture recognition in the context of human-computer interaction

The paper deals with an artificial-vision based system for simultaneous people tracking and posture recognition in the context of human-computer interaction. We adopt no particular assumptions on the movement of a person and on its appearance, making the system suitable to several real-world applications. The system can be roughly subdivided into tw6 highly correlated phases: tracking and recognition. The tracking phase is concerned with establishing coherent relations of the same subject between frames. We adopted the Condensation algorithm due to its robustness in highly cluttered environments. The recognition phase adopts a modified eigenspace technique in order to classify between several different human postures and permits to exploit a rich interaction between a human user and a robotic system by means of a set of natural gestures. Experimental results have demonstrated the robustness of our system in different working conditions.