Real-time Multiple Head Tracking Using Texture and Colour Cues

We address the task of monocular visual head tracking in the context of applications that involve human-robot interactions, where both near field and far field tracking settings could occur and real-time constraints are imposed. The original contribution of this paper is a real-time multiperson tracking model that combines a priori texture and colour models for different head poses with face detectors for different face orientations. We show that such a combination improves tracker performance significantly. At the same time the proposed model takes into account major difficulties that are related to real-time data processing (non-uniform observations, processing time restrictions). The model is evaluated on a set of realistic scenarios recorded on a humanoid robot that involve interactions between the robot and the participants with robot motion, unconstrainted displacement of the participants, lighting variations etc. The algorithm runs in real-time and shows significant improvement of performance.