Dynamic shape outlier detection for human locomotion

Dynamic human shape in video contains rich perceptual information, such as the body posture, identity, and even the emotional state of a person. Human locomotion activities, such as walking and running, have familiar spatiotemporal patterns that can easily be detected in arbitrary views. We present a framework for detecting shape outliers for human locomotion using a dynamic shape model that factorizes the body posture, the viewpoint, and the individual's shape style. The model uses a common embedding of the kinematic manifold of the motion and factorizes the shape variability with respect to different viewpoints and shape styles in the space of the coefficients of the nonlinear mapping functions that are used to generate the shapes from the kinematic manifold representation. Given a corrupted input silhouette, an iterative procedure is used to recover the body posture, viewpoint, and shape style. We use the proposed outlier detection approach to fill in the holes in the input silhouettes, and detect carried objects, shadows, and abnormal motions.