Information Technology Reference
In-Depth Information
method is very fast because of its simplicity, allowing us to integrate it in a real
time system.
As a future work, we propose to measure the achieved performance with the
current features in comparison with state of the art.
Acknowledgements
This research has been partially supported by the Spanish projects TIN2008-
06890-C02-02.
References
1. Wang, L., Suter, D.: Visual learning and recognition of sequential data manifolds
with applications to human movement analysis. Computer Vision and Image Un-
derstanding 110, 153-172 (2008)
2. Schindler, K., Gool, L.: Action Snippets: How many frames does human action
recognition require? In: IEEE Conference on Computer Vision and Pattern Recog-
nition (CVPR 2008) (2008)
3. Ahmad, M., Lee, S.W.: Human action recognition using shape and CLG-motion
flow from multi-view image sequences. Pattern Recognition 41, 2237-2252 (2008)
4. Zhou, H., Wang, L., Suter, D.: Human action recognition by feature-reduced Gaus-
sian preocess classification. Pattern Recognition Letters (2009)
5. Lv, F., Nevatia, R.: Single View human Action Recognition using Key pose Match-
ing and Viterbi Path Searching. In: IEEE Conference on Computer Vision and
Pattern Recognition, CVPR 2007, June 17-22, pp. 1-8 (2007)
6. Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM ap-
proach. In: Proceedings of the 17th International Conference on Pattern Recogni-
tion, ICPR 2004, August 23-26, vol. 3, pp. 32-36 (2004)
7. Parameswaran, V., Chellappa, R.: View invariants for human action recognition.
In: Proceedings of IEEE Computer Society Conference on Computer Vision and
Pattern Recognition, June 18-20, vol. 2, pp. II- 613-II-619 (2003)
8. Yan, K., Sukthankar, R., Hebert, M.: Ecient visual event detection using vol-
umetric features. In: Tenth IEEE International Conference on Computer Vision,
ICCV 2005, October 17-21, vol. 1, pp. 166-173 (2005)
9. Ali, S., Basharat, A., Shah, M.: Chaotic Invariants for Human Action Recognition.
In: IEEE 11th International Conference on Computer Vision, ICCV 2007, October
14-21, pp. 1-8 (2007)
10. Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM ap-
proach. In: Proceedings of the 17th International Conference on Pattern Recogni-
tion, ICPR 2004, August 23-26, vol. 3, pp. 32-36 (2004)
11. Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as Space-
Time Shapes. Transactions on Pattern Analysis and Machine Intelligence 29(12),
2247-2253 (2007)
12. Gordon, N.J., Salmond, D.J., Smith, A.F.M.: Novel approach to nonlinear/non-
Gaussian Bayesian state estimation. IEE Proceedings F Radar & Signal Processing
140 2, 107-113 (1993)
13. Moscato, P.: Memetic Algorithms: a short introduction. In: Corne, D., Dorigo, M.,
Glover, F. (eds.) New Ideas in Optimization, pp. 219-234. McGraw Hill, New York
(1999)
Search WWH ::
Custom Search