A Human Body Model for Articulated 3D Pose Tracking

Within the last decade, robotic research has turned more and more towards flexible assistance and service applications. Especially when cooperating with untrained persons at small distances in the same workspace, it is essential for the robot to have a deep understanding and a reliable hypothesis of the intentions, activities and movements of the human interaction partner. With growing computational capacities and new emerging sensor technologies, methods for tracking of articulated motion have become a hot topic of research. Tracking of the human body pose (often also referred to as Human Motion Capture) without invasive measurement techniques like attaching markers or accelerometers and gyroscopes demands (1) for algorithms that maximally exploit sensor data to resolve ambiguities that compulsorily arise in tracking of a high-degree-of-freedom system, and (2) for strong models of the tracked body that constrain the search space enough to enable fast and online tracking. This chapter proposes a 3D model for tracking of the human body, along with an iterative tracking approach. The body model is composed of rigid geometric limb models, and joint models based on an elastic band approach. The joint model allows for different joint types with different numbers of degrees of freedom. Stiffness and adhesion can be controlled via joint parameters. Effectiveness and efficiency of these models are demonstrated by applying them within an Iterative Closest Point (ICP) approach for tracking of the human body pose. Used sensors include a Time-of-Flight camera (depth camera), a mono colour camera as well as a laser range finder. Model and sensor information are integrated within the same tracking step for optimal pose estimation, and the resulting fusion process is explained, along with the used sensor model. The presented tracking system runs online at 20-25 frames per second on a standard PC. We first describe related work and approaches, which partially form the basis for the presented models and methods. Then, a brief introduction into the ICP is given. The model for body limbs and joints is explained in detail, followed by a description of the full tracking algorithm. Experiments, examples and different evaluations are given. The chapter closes with a discussion of the achieved results and a conclusion.