Detecting Hand-Ball Events in Video
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We analyze videos in which a hand interacts with a basketball. In this work, we present a computational system which detects and classifies hand-ball events, given the trajectories of a hand and ball. Our approach is to determine non-gravitational parts of the ball's motion using only the motion of the hand as a reliable cue for hand-ball events. This thesis makes three contributions. First, we show that hand motion can be segmented using piecewise fifth-order polynomials inspired by work in motor control. We make the remarkable experimental observation that hand-ball events have a phenomenal correspondence to the segmentation breakpoints. Second, by fitting a context-dependent gravitational model to the ball over an adaptive window, we can isolate places where the hand is causing non-gravitational motion of the ball. Finally, given a precise segmentation, we use the measured velocity steps (force impulses) on the ball to detect and classify various event types.