Descriptive temporal template features for visual motion recognition

Pattern Recognition Letters

Volumn 30, Issue 12, 2009, Pages 1049-1058

  Meng, Hongying ^a   Pears, Nick ^b  

^a UNIVERSITY OF LINCOLN   (United Kingdom)

^b UNIVERSITY OF YORK   (United Kingdom)

Author keywords

Embedded vision; Event recognition; Gesture recognition; Machine learning; Motion analysis

Indexed keywords

CLASSIFICATION PERFORMANCE; EMBEDDED APPLICATION; EMBEDDED VISION; EVENT RECOGNITION; FEATURE VECTORS; HIGH-SPEED; HUMAN-ACTION RECOGNITION; LOW DIMENSIONAL; MACHINE LEARNING; MOTION ANALYSIS; MOTION HISTORY; MOTION HISTORY IMAGES; MOTION INFORMATION; TEMPORAL TEMPLATES; VISUAL MOTION;

IMAGE RETRIEVAL; ROBOT LEARNING; SUPPORT VECTOR MACHINES;

GESTURE RECOGNITION;

EID: 67650248998     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2009.03.003     Document Type: Article

References (33)

1. Aggarwal J.K., and Cai Q. Human motion analysis: A review. Computer Image and Vision Understanding 73 3 (1999) 428-440
2. Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R., 2005. Actions as space-time shapes. In: ICCV, pp. 1395-1402.
3. Bobick A.F., and Davis J.W. The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. Machine Intell. 23 3 (2001) 257-267
4. Bradski G.R., and Davis J.W. Motion segmentation and pose recognition with motion history gradients. Machine Vision Appl. 13 3 (2002) 174-184
5. Campbell, L.W., Bobick, A.F., 1995. Recognition of human body motion using phase space constraints. In: ICCV, pp. 624-630.
6. Cristianini N., and Shawe-Taylor J. An Introduction to Support Vector Machines (and other kernel-based learning methods) (2000), Cambridge University Press, Cambridge, UK
7. Dalal, N., Triggs, B., Schmid, C., 2006. Human detection using oriented histograms of flow and appearance. In: ECCV (2), pp. 428-441.
8. Davis, J.W., 2001. Hierarchical motion history images for recognizing human motion. In: IEEE Workshop on Detection and Recognition of Events in Video, pp. 39-46.
9. Davis J.W., and Tyagi A. Minimal-latency human action recognition using reliable-inference. Image Vision Comput. 24 5 (2006) 455-472
10. Dollár, P., Rabaud, V., Cottrell, G., Belongie, S., 2005. Behavior recognition via sparse spatio-temporal features. In: VS-PETS.
11. Efros, A.A., Berg, A.C., Mori, G., Malik, J., 2003. Recognizing action at a distance. In: ICCV, pp. 726-733.
12. Farmer J., Casdagli M., Eubank S., and Gibson J. State-space reconstruction in the presence of noise. Physica D 51 (1991) 52-98
13. Gao, J., Hauptmann, A.G., Bharucha, A., Wactlar, H.D., 2004. Dining activity analysis using a hidden markov model. In: ICPR (2), pp. 915-918.
14. Green R.D., and Guan L. Quantifying and recognizing human movement patterns from monocular video images - Part II: Applications to biometrics. IEEE Trans. Circuits System Video Technol. 14 2 (2004) 191-198
15. Hastie, T., Rosset, S., Tibshirani, R., Zhu, J., 2004. The entire regularization path for the support vector machine. .
16. Joachims, T., 1999. Making large-scale SVM learning practical. In: Oikonomopoulos, Antonios, Patras, Ioannis, Pantic, Maja (Eds.), Advances in Kernel Methods - Support Vector Learning. MIT-Press, USA. .
17. Ke, Y., Sukthankar, R., Hebert., M., 2005. Efficient visual event detection using volumetric features. In: ICCV, Beijing, China, October 15-21, 2005, pp. 166-173.
18. Meng, H., Pears, N., Bailey, C., 2006a. Human action classification using SVM_2K classifier on motion features. In: LNCS, vol. 4105. Istanbul, Turkey, pp. 458-465.
19. Meng, H., Pears, N., Bailey, C., 2006b. Recognizing human actions based on motion information and SVM. In: 2nd IET Internat. Conf. on Intelligent Environments. IET, Athens, Greece, pp. 239-245.
20. Meng, H., Pears, N., Bailey, C., 2007a. A human action recognition system for embedded computer vision application. In: CVPR Workshop on Embedded Computer Vision.
21. Meng, H., Pears, N., Bailey, C., 2007b. Motion information combination for fast human action recognition. In: 2nd Internat. Conf. on Computer Vision Theory and Applications (VISAPP07), Barcelona, Spain.
22. Meng H., Freeman M., Pears N., and Bailey C. Real-time human action recognition on an embedded, reconfigurable video processing architecture. J. Real-Time Image Process. 3 3 (2008) 163-176
23. Moeslund T., Hilton A., and Kruger V. A survey of advances in vision-based human motion capture and analysis. Computer Image and Vision Understanding 103 2-3 (2006) 90-126
24. Nascimento, J.C., Figueiredo, M.A.T., Marques, J.S., 2005. Recognition of human activities using space dependent switched dynamical systems. In: IEEE Internat. Conf. on Image Processing, ICIP.
25. Niebles, J., Wang, H., Fei-Fei, L., 2006. Unsupervised learning of human action categories using spatial-temporal words. In: BMVC06, p. III:1249.
26. Ogata T., Tan J.K., and Ishikawa S. High-speed human motion recognition based on a motion history image and an eigenspace. IEICE Trans. Inform. Systems E89 1 (2006) 281-289
27. Oikonomopoulos, A., Patras, I., Pantic, M., 2006. Kernel-based recognition of human actions using spatiotemporal salient points. In: Proc. of CVPR Workshop 06, vol. 3, pp. 151-156. .
28. Schuldt, C., Laptev, I., Caputo, B., 2004. Recognizing human actions: A local SVM approach. In: ICPR, Cambridge, UK.
29. Stauffer C., and Grimson W.E.L. Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Machine Intell. 22 8 (2000) 747-757.
30. Weinland, D., Ronfard, R., Boyer, E., 2005. Motion history volumes for free viewpoint action recognition. In: IEEE Internat. Workshop on modeling People and Human Interaction (PHI'05). .
31. Wong, S.-F., Cipolla, R., 2005. Real-time adaptive hand motion recognition using a sparse bayesian classifier. In: ICCV-HCI, pp. 170-179.
32. Wong, S.-F., Cipolla, R., 2006. Continuous gesture recognition using a sparse bayesian classifier. In: ICPR (1), pp. 1084-1087.
33. Yeo, C., Ahammad, P., Ramchandran, K., Sastry, S., 2006. Compressed domain real-time action recognition. In: IEEE Internat. Workshop on Multimedia Signal Processing (MMSP) - 2006. IEEE, Washington, DC, USA.