Combining discriminative features to infer complex trajectories

ACM International Conference Proceeding Series

Volumn 148, Issue , 2006, Pages 761-768

  Ross, David A ^a   Osindero, Simon ^a   Zemel, Richard S ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ACQUISITION; FEATURE EXTRACTION; MAPPING; PARAMETER ESTIMATION; PROBABILISTIC LOGICS; TRACKING (POSITION);

DISCRIMINATIVE FEATURES; DYNAMICS EXPERTS; INFER COMPLEX TRAJECTORIES;

MATHEMATICAL MODELS;

EID: 34250781671     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1143844.1143940     Document Type: Conference Paper

References (14)

1. Collins, R., Liu, Y., & Leordeanu, M. (2005). On-line selection of discriminative tracking features. IEEE Transaction on Pattern Analysis and Machine Intelligence, 27, 1631 - 1643.
2. Forsyth, D. A., & Ponce, J. (2002). Computer vision: A modern approach. Prentice Hall.
3. Ghahramiani, Z., & Hinton, G. E. (2000). Variational learning for switching state-space models. Neural Computation, 12, 831-864.
4. Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural Computation, 14, 1771-1800.
5. Isard, M., & Blake, A. (1998). A mixed-state Condensation tracker with automatic model-switching. Proceedings of the International Conference on Computer Vision (ICCV).
6. Lafferty, J., McCallum, A., & Pereira, F. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. International Conference on Machine Learning (ICML).
7. Lim, J., Ross, D. A., Lin, R.-S., & Yang, M.-H. Iucremental learning for visual tracking. In L. K. Saul, Y. Weiss and L. Bottou (Eds.), Nips 17.
8. Quattoni, A., Collins, M., & Darrell, T. Conditional random fields for object recognition. In L. K. Saul, Y. Weiss and L. Bottou (Eds.), Nips 17.
9. Roth, S., & Black, M. J. (2005). Fields of experts: A framework for learning image priors. CVPR.
10. Sminchisescu, C., Kanaujia, A., Li, Z., & Metaxas, D. (2005). Discrimintative density propagation for 3d human motion estimation. CVPR.
11. Sudderth, E. B., Mandel, M. I., Freeman, W. T., & Willsky, A. S. Distributed occlusion reasoning for tracking with nonparametric belief propagation. In L. K. Saul, Y. Weiss and L. Bottou (Eds.), Nips 17.
12. Taycher, L., Shakhnarovich, G., Demirdjian, D., & Darrell, T. (2005). Condition random people: Tracking humans wth crfs and grid filters (Technical Report 2005-079). MIT-CSAIL.
13. Teh, Y., Welling, M., Osindero, S., & Hinton, G. E. (2003). Energy-based models for sparse overcomplete representations. Journal of Machine Learning Research, 4, 1235-1260.
14. Welling, M., Rosen-Zvi, M., & Hinton, G. Exponential family harmoniums with an application to information retrieval. In L. K. Saul, Y. Weiss and L. Bottou (Eds.), Nips 17.