Recognizing household activities from human motion data using active learning and feature selection

Technology and Disability

Volumn 22, Issue 1-2, 2010, Pages 17-26

  Zhao, Liyue ^a   Wang, Xi ^a   Sukthankar, Gita ^a  

^a University of Central Florida   (United States)

Author keywords

Active learning; Activity recognition; Conditional random fields; Feature selection; Support vector machines

Indexed keywords

ACCURACY; ARTICLE; DATA BASE; HOUSEHOLD; LEARNING; MOTION ANALYSIS SYSTEM; PHYSICAL ACTIVITY; SEQUENCE ANALYSIS; TRAINING;

EID: 77953763789     PISSN: 10554181     EISSN: None     Source Type: Journal    
DOI: 10.3233/TAD-2010-0284     Document Type: Article

References (17)

1. O. Arikan, D. Forsyth and J. OBrien, Motion synthesis from annotations, ACM Trans Graphics 22(3) (2003).
2. J. Barbic, A. Safonova, J.-Y. Pan, C. Faloutsos, J. Hodgins and N. Pollard. Segmenting motion capture data into distinct behaviors, In Proceedings of Graphics Interface, 2004.
3. S. Canu, Y. Grandvalet, V. Guigue and A. Rakotomamonjy, SVM and Kernel Methods Matlab Toolbox, Perception Systemes et Information, INSA de Rouen, Rouen, France, 2005.
4. O. Chapelle, V. Sindhwani and S. Keerthi, Optimization techniques for semi-supervised support vector machines, Journal of Machine Learning Research 9(203C) (2008), 233.
5. B. Chiu, E. Keogh and S. Lonardi, Probabilistic discovery of time series motifs, In Proc. ACM KDD, 2003.
6. F. Frade et al., Guide to the CMU Multimodal Activity Database. Technical Report RI-08-22, CMU, 2008.
7. J. Lafferty et al., Kernel conditional random fields: representation and clique selection, In ICML, 2001.
8. A. Levin and T. Weiss, Learning to combine bottom-up and top-down segmentation, In ECCV, 2006.
9. D. Liu and J. Nocedal, On the limited memory model for large scale optimization, Mathematical Programming B 45(3) (1989).
10. D. Minnen et al., Discovering characteristic actions from onbody sensor data, In ISWC, 2006.
11. V. Sindhwani, P. Niyogi and M. Belkin, Beyond the point cloud: frame transductive to semi-supervised learning, In Proceedings of the International Conference on Machine Learning (ICML), 2005.
12. C. Sminchisescu, A. Kanaujia, Z. Li and D. Metaxas, Conditional model for contextual human motion recognition, In ICCV, 2005.
13. Y. Tanaka, K. Iwamoto and K. Uehara, Discovery of timeseries motif from multi-dimensional data based on mdl principle, Machine Learning 58(2) (2005).
14. S. Tong and D. Koller, Support vector machine active learning with applications to text classification, Journal of Machine Learning Research 2(45C66) (2002).
15. A. Vahdatpour et al., Toward unsupervised activity discovery using multidimensional motif detection in time series, In IJCAI, 2009.
16. D. Vail et al., Feature selection for activity recognition in multi-robot domains, In AAAI, 2008.
17. F. Zhou, F. Frade and J. Hodgins, Aligned cluster analysis for temporal segmentation of human motion, In Proceedings of the IEEE Conference onAutomaticFace and Gesture Recognition, 2008.