Action Recognition Using Ballistic DynamicsShiv N. Vitaladevuni, Vili Kellokumpu and Larry S. Davis |
Supplementary recognition results: |
Gesture Recognition Results |
Dataset consists of 14 army signaling gestures [Shet04]. Each gesture is performed 5 times by 5 subjects. The ballistic action recognition framework using Motion History Image features achieved classification rate of 92%, compared to previously published rate of 84%. Recognition using MHI features without ballistic segmentation gave 73% accuracy. This highlights the utility and consistency of the ballistic segmentation. |
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Classification Results |
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Tennis Video Example |
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Tennis video taken from [Efros et al. '03]. The video had 5 forehand swing shots, all were detected correctly. Animations of the ballistic segments obtained for 2 of the forehands: |
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1. Frames 285 to 295 |
2. Frames 295 to 308 |
3. Frames 308 to 316 |
4. Frames 316 to 329 |
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1. Frames 455 to 464 |
2. Frames 464 to 470 |
3. Frames 470 to 484 |
4. Frames 484 to 498 |
Dance Video Example |
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Tutorial video for a dance called "Grapevine Pop", courtesy www.fitmoves.com, duration 30 seconds, ~790 frames. The tutor performs 4 cycles of dance steps; first to her right, then a symmetric set of steps to her left, followed by another cycle to her right and one more to her left. Animations of the ballistic segments computed by the approach are shown here, each row corresponds to a cycle. The animated gifs are synchronized according to the movement. The consistency of the computed segments indicates the appropriateness of ballistic dynamics for the analysis. In the second cycle, segment boundary is missed because the tutor jumps almost out of the frame; this affected the optical flow computation. Click here to view the original video. |
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