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.
| Identifer | oai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/3904 |
| Date | January 2008 |
| Creators | Miller, Nicholas |
| Source Sets | University of Waterloo Electronic Theses Repository |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
Page generated in 0.003 seconds