Ballistic Hand Movements
Common movements like reaching, striking, etc. observed during surveillance have highly variable target locations. This puts appearance-based techniques at a disadvantage for modelling and recognizing them. Psychological studies indicate that these actions are ballistic in nature. Their trajectories have simple structures and are determined to a great degree by the starting and ending positions. We present an approach for movement recognition that explicitly considers their ballistic nature. This enables the decoupling of recognition from the movement’s trajectory, allowing generalization over a range of target-positions. A given movement is first analyzed to determine if it is ballistic. Ballistic movements are further classified into reaching, striking, etc. The proposed approach was tested with motion capture data obtained from the CMU MoCap database.
KeywordsExecution Plan Ballistic Movement Capture Sequence Motion Capture Data Communicative Gesture
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- 1.Smyth, I., Wing, M. (eds.): The Psychology of Human Movement. Academic Press Inc., Orlando, FL 32887 (1984)Google Scholar
- 3.Asatryan, D.G., Fel’dman, A.G.: Functional tuning of the nervous system with control of movement or maintenance of a steady posture. Biophysics 1, 925–935 (1965)Google Scholar
- 4.Cooke, J.D.: The organization of simple, skilled movements. In: Stelmach, G.E., Requin, J. (eds.) Tutorials in Motor Behavior, pp. 199–211 (1980)Google Scholar
- 7.Parameswaran, V., Chellappa, R.: View invariants for human action recognition. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2003), vol. 2, pp. 613–619 (2003)Google Scholar
- 9.Yilmaz, A., Shah, M.: Recognizing human action in videos acquired by uncaliberated moving cameras. In: Proc. IEEE Int’l Conf. Computer Vision (ICCV 2005) (2005)Google Scholar
- 10.Elgammal, A., Shet, V.D., Yacoob, Y., Davis, L.S.: Learning dynamics for exemplar-based gesture recognition. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2003), June 18-20, 2003, vol. 1, pp. 571–578 (2003)Google Scholar
- 11.Yilmaz, A., Shah, M.: Actions as objects: A nover action representation. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2005) (2005)Google Scholar
- 14.Bregler, C.: Learning and recognizing human dynamics in video sequences. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 1997) (1997)Google Scholar
- 15.Pavlovic, V., Rehg, J.M., MacCormick, J.: Learning switching linear models for human motion. In: Proc. Neural Information Processing Systems (NIPS 2000), pp. 981–987 (2000)Google Scholar