nips nips2000 nips2000-82 nips2000-82-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Dirk Ormoneit, Hedvig Sidenbladh, Michael J. Black, Trevor Hastie
Abstract: We present methods for learning and tracking human motion in video. We estimate a statistical model of typical activities from a large set of 3D periodic human motion data by segmenting these data automatically into
[1] A. Bobick and J. Davis. An appearance-based representation of action. ICPR, 1996.
[2] T-J. Cham and J. Rehg. A multiple hypothesis approach to figure tracking. CVPR, pp. 239- 245, 1999.
[3] M. Isard and A. Blake. Contour tracking by stochastic propagation of conditional density. ECCV, pp. 343-356, 1996.
[4] M. E. Leventon and W. T. Freeman. Bayesian estimation of 3-d human motion from an image sequence. Tech. Report TR-98-06, Mitsubishi Electric Research Lab, 1998.
[5] D. Ormoneit, H. Sidenbladh , M. Black, T. Hastie, Learning and tracking human motion using functional analysis, submitted: IEEE Workshop on Human Modeling, Analysis and Synthesis, 2000.
[6] S.M. Seitz and C.R. Dyer. Affine invariant detection of periodic motion. CVPR, pp. 970-975, 1994.
[7] H. Sidenbladh, M. J. Black, and D. J. Fleet. Stochastic tracking of 3D human figures using 2D image motion. to appear, ECCV-2000, Dublin Ireland.
[8] Y. Yacoob and M. Black. Parameterized modeling and recognition of activities in temporal surfaces. CVIU, 73(2):232-247, 1999.
[9] G. Sherlock, M. Eisen, O. Alter , D. Botstein, P. Brown, T. Hastie, and R. Tibshirani.