Real-time Model-based Articulated Object Pose Detection and Tracking with Variable Rigidity Constraints

Karl Pauwels, Leonardo Rubio, Eduardo Ros; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 3994-4001

A novel model-based approach is introduced for real-time detection and tracking of the pose of general articulated objects. A variety of dense motion and depth cues are integrated into a novel articulated Iterative Closest Point approach. The proposed method can independently track the six-degrees-of-freedom pose of over a hundred of rigid parts in real-time while, at the same time, imposing articulation constraints on the relative motion of different parts. We propose a novel rigidization framework for optimally handling unobservable parts during tracking. This involves rigidly attaching the minimal amount of unseen parts to the rest of the structure in order to most effectively use the currently available knowledge. We show how this framework can be used also for detection rather than tracking which allows for automatic system initialization and for incorporating pose estimates obtained from independent object part detectors. Improved performance over alternative solutions is demonstrated on real-world sequences.