Recognition of human activities using SVM multi-class classifier

Pattern Recognition Letters

Volumn 31, Issue 2, 2010, Pages 100-111

  Qian, Huimin ^a   Mao, Yaobin ^a   Xiang, Wenbo ^a   Wang, Zhiquan ^a  

^a NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Background subtraction; CCMEI; Decision tree classifier; Human activity recognition; Support vector machine

Indexed keywords

BACKGROUND SUBTRACTION; BINARY TREE ARCHITECTURE; BOUNDING BOX; CLASSIFICATION PERFORMANCE; CLUSTERING PROCESS; DATA SETS; DECISION TREE CLASSIFIER; DECISION TREE CLASSIFIERS; HIERARCHICAL CLASSIFICATION; HIGH ROBUSTNESS; HUMAN ACTIVITIES; HUMAN ACTIVITY RECOGNITION; MOTION ENERGY; MULTI-CLASS CLASSIFIER; SYSTEM FRAMEWORK;

BINARY TREES; CLASSIFIERS; CLUSTERING ALGORITHMS; COMPUTER VISION; DECISION TREES; FEATURE EXTRACTION; FORESTRY; IMAGE CODING; IMAGE RETRIEVAL;

SUPPORT VECTOR MACHINES;

ALGORITHMS; CLASSIFIERS; CODING; DECISION MAKING; FORESTRY; IMAGE ANALYSIS; TREES;

EID: 71549159433     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2009.09.019     Document Type: Article

References (49)

1. Aggarwal J.K., and Cai Q. Human motion analysis: A review. Computer Vision and Image Understanding 73 3 (1999) 428-440
2. Ali, A., Aggarwal, J.K., 2001. Segmentation and recognition of continuous human activity. In: IEEE Workshop on Detection and Recognition of Events in Video, Vancouver, BC, Canada, pp. 28-35.
3. Ben-Arie J., Wang Z., Pandit P., and Rajaram S. Human activity recognition using multidimensional indexing. IEEE Trans. Pattern Anal. Machine Intell. 24 8 (2002) 1091-1104
4. 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
5. Cheong S., Oh S.H., and Lee S.Y. Support vector machines with binary tree architecture for multi-class classification. Neural Inform. Process.: Lett. Rev. 2 3 (2004) 47-51
6. Dietterich T.G., and Bakiri G. Solving multiclass learning problems via error-correcting output codes. J. Artif. Intell. Res. 2 (1995) 263-286
7. Dollár, P., Rabaud, V., Cottrell, G., Belongie, S., 2005. Behavior recognition via sparse spatio-temporal features. In: IEEE Internat. Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 65-72.
8. Efros, A.A., Berg, A.C., Mori, G., Malik, J., 2003. Recognizing action at a distance. In: IEEE Internat. Conf. on Comput. Vision, vol. 2, pp. 726-733.
9. Elgammal, A., Harwood, D., Davis, L., 2000. Non-parametric model for background subtraction. In: ECCV, vol. 1843, pp. 751-767.
10. Ermes M., Pärkkä J., Mäntyjärvi J., and Korhonen I. Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Trans. Inform. Technol. Biomed. 12 1 (2008) 20-26
11. Feng, X., Perona, P., 2002. Human action recognition by sequence of movelet codewords. In: Internat. Symposium on 3D Data Processing Visualization and Transmission, pp. 717-721.
12. Gavrila D.M. The visual analysis of human movement: A survey. Computer Vision and Image Understanding 73 1 (1999) 82-98
13. Hsu C.W., and Lin C.J. A comparison of methods for multi-class support vector machines. IEEE Trans. Neural Networks 13 2 (2002) 415-425
14. Ke, Y., Sukthankar, R., Hebert, M., 2005. Efficient visual event detection using volumetric features. In: IEEE ICCV, vol. 1, pp. 166-173.
15. Kellokumpu, V., Pietikäinen, M., Heikkilä, J., 2005. Human activity recognition using sequences of postures. In: IAPR Conf. on Machine Vision Applications, pp. 570-573.
16. Kreßel U.H.G. Pairwise Classification and Support Vector Machines (1999) 255-268
17. Lee, K.K., Xu, Y., 2004. Modeling human actions from learning. In: IEEE/RSJ Internat. Conf. on Intelligent Robots and Systems, pp. 2787-2792.
18. Madabhushi, A., Aggarwal, J.K., 1999. A bayesian approach to human activity recognition. In: IEEE Workshop on Visual Surveillance, pp. 25-32.
19. Maurer, U., Smailagic, A., Siewiorek, D.P., Deisher, M., 2006. Activity recognition and monitoring using multiple sensors on different body positions. In: Internat. Workshop on Wearable and Implantable Body Sensor Networks, pp. 113-116.
20. Meng H.Y., and Pears N. Descriptive temporal template features for visual motion recognition. Pattern Recognition Lett. 30 12 (2009) 1049-1058
21. Min, J., Kasturi, R., 2004. Activity recognition based on multiple motion trajectories. In: 17th Internat. Conf. on Pattern Recognition.
22. Moeslund T.B., Hiltonb A., and Krüger V. A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 104 2-3 (2006) 90-126
23. Mukundan R., and Ramakrishnan K.R. Fast computation of legendre and zernike moments. Pattern Recognition 28 9 (1995) 1433-1442
24. Nakajima, C., Itoh, N., Pontil, M., Poggio, T., 2000. Object recognition and detection by a combination of support vector machine and rotation invariant phase only correlation. In: Internat. Conf. on Pattern Recognition, IEEE, vol. 4, pp. 4787-4790.
25. Ng A.Y., and Jordan M.I. On discriminative vs. generative classifiers: A comparison of logistic regression and Naive Bayes. Adv. Neural Inform. Process. Syst. 14 (2002) 841-848
26. Niebles J.C., Wang H., and Li F. Unsupervised learning of human action categories using spatial-temporal words. Internat. J. Computer Vision 79 (2008) 299-318
27. Niu, F., Abdel-Mottaleb, M., 2005. Hmm-based segmentation and recognition of human activities from video sequences. In: IEEE Internat. Conf. on Multimedia and Expo, pp. 804-807.
28. Platt J.C., Cristianini N., and Shawe-Taylor J. Large margin dags for multiclass classification. Adv. Neural Inform. Process. Syst. 12 (2000) 547-553
29. Poppe R. Vision-based human motion analysis: An overview. Computer Vision and Image Understanding 108 1-2 (2007) 4-18
30. Psarrou A., Gong S., and Walter M. Recognition of human gestures and behavior based on motion trajectories. Image Vision Comput. 20 (2002) 349-358
31. Qian, H., Mao, Y., Xiang, W., Wang, Z., 2008. Home environment fall detection system based on a cascaded multi-SVM classifier. In: Internat. Conf. on Control, Automation, Robotics and Vision, pp. 1567-1572.
32. Rennie, J.D., Rifkin, R., 2001. Improving multiclass text classification with the support vector machine. AI Memo AIM-2001-026, Massachusetts Institute of Technology.
33. Rubinstein, Y.D., Hastie, T., 1997. Discriminative vs. informative learning. In: Proc. Third Internat. Conf. on Knowledge Discovery and Data Mining, pp. 49-53.
34. Schüldt, C., Laptev, I., Caputo, B., 2004. Recognizing human actions: A local svm approach. In: 17th Internat. Conf. on Pattern Recognition, vol. 3, pp. 32-36.
35. Takahashi, F., Abe, S., 2002. Decision-tree-based multiclass support vector machines. In: Ninth Internat. Conf. on Neural Information Processing, vol. 3, pp. 1418-1422.
36. Vapnik V. The Nature of Statistical Learning Theory (1995), Springer-Verlag, New York
37. Vapnik V. Statistical Learning Theory (1998), John Wiley and Sons Inc., New York
38. Veeraraghavan A., Roy-Chowdhury A.K., and Chellappa R. Matching shape sequences in video with applications in human movement analysis. IEEE Trans. Pattern Anal. Machine Intell. 27 12 (2005) 1896-1909
39. Wang L., Hu W.M., and Tan T.N. Recent developments in human motion analysis. Pattern Recognition 36 3 (2003) 585-601
40. Wang, Y., Huang, K.Q., Tan, T.N., June 2007. Human activity recognition based on r transform. In: IEEE Conf. on CVPR. pp. 1-8.
41. Weinland D., Ronfarda R., and Boyer E. Free viewpoint action recognition using motion history volumes. Computer Vision and Image Understanding 104 2-3 (2006) 249-257
42. Weston, J., Watkins, C., 1999. Multi-class support vector machines. In: Proc. ESANN, pp. 1-2.
43. Xiang, T., Gong, S., Parkinson, D., 2002. Autonomous visual events detection and classification without explicit object-centred segmentation and tracking. In: BMVC, pp. 133-242.
44. Yacoob Y., and Black M.J. Parameterized modelling and recognition of activities. Computer Vision and Image Understanding 73 2 (1999) 232-247
45. Yang Y., and Pedersen J.O. A comparative study on feature selection in text categorization. Proc. 14th Internat. Conf. on Machine Learning (1997), Morgan Kaufmann Publishers Inc., San Francisco, CA, USA 412-420
46. Yeo,Y., Ahammad, P., Ramchandran, K., Sastry, S.S., 2006. Compressed domain real-time action recognition. In: IEEE International Workshop in Multimedia Signal Processing. IEEE, Victoria, BC, USA, pp. 33-36.
47. Yilmaz, A., Shah, M., 2005a. Actions sketch: A novel action representation. In: IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 984- 989.
48. Yilmaz, A., Shah, M., 2005b. Recognizing human actions in videos acquired by uncalibrated moving cameras. In: IEEE Internat. Conf. on Computer Vision, vol. 1, pp. 150-157.
49. Zaidenberg, S., Brdiczka, O., Reignier, P., Crowley, J., 2006. Learning context models for the recognition of scenarios. In: IFIP Conf. on Artificial Intelligence Applications and Innovations, vol. 204, pp. 86-97.