Multiple-person tracking system for content analysis

This paper presents a framework to track multiple persons in real-time. First, a method with real-time and adaptable capability is proposed to extract face-like regions based on skin, motion and silhouette features. Then, an adaptable skin model is used for each detected face to overcome the changes of the observed environment. After that, a two-stage face verification algorithm is proposed to quickly eliminate false faces based on face geometries and the SVM (Support Vector Machine) approach. In order to overcome the effect of lighting changes, during verification, a method of color constancy compensation is proposed. Then, a robust tracking scheme is applied to identify multiple persons based on a face-status table. With the table, the proposed system has powerful capabilities to track different persons at different statuses, which is quite important in face-related applications. Experimental results show that the proposed method is more robust and powerful than other traditional methods, which utilize only color, motion information, and the correlation technique.