Detecting actions by integrating sequential symbolic and sub-symbolic information in human activity recognition

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7376 LNAI, Issue , 2012, Pages 394-404

  Glodek, Michael ^a   Schwenker, Friedhelm ^a   Palm, Günther ^a  

^a UNIVERSITY OF ULM   (Germany)

Author keywords

Action Recognition; Layered Architecture; Machine Learning; Markov Models

Indexed keywords

ACTION RECOGNITION; BODY PARTS; CLASSICAL APPROACH; CONTEXT INFORMATION; DATA SETS; ELEMENTARY ACTIONS; FEATURE SPACE; HIERARCHICAL STRUCTURES; HUMAN ACTIVITIES; HUMAN ACTIVITY RECOGNITION; LAYERED ARCHITECTURE; MARKOV MODEL; SEQUENTIAL ALGORITHM; SUB-SYMBOLIC;

DATA MINING; HIDDEN MARKOV MODELS; IMAGE RECOGNITION; MOTION ESTIMATION;

LEARNING SYSTEMS;

EID: 84864929446     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-31537-4_31     Document Type: Conference Paper

References (19)

1. Ben-Arie, J., Wang, Z., Pandit, P., Rajaram, S.: Human activity recognition using multidimensional indexing. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(8), 1091-1104 (2002)
2. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2006)
3. Brand, M., Oliver, N., Pentland, A.: Coupled hidden markov models for complex action recognition. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), pp. 994-999. IEEE (1997)
4. Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3), 27 (2011)
5. Fine, S., Singer, Y., Tishby, N.: The hierarchical hidden Markov model: Analysis and applications. Machine Learning 32(1), 41-62 (1998)
6. Freeman, W.T., Roth, M.: Orientation histograms for hand gesture recognition. Technical Report TR94-03, Mitsubishi Electrical Research Laboratories, originally published at the International Workshop on Automatic Face and Gesture Recognition (1995)
7. Glodek, M., Bigalke, L., Schels, M., Schwenker, F.: Incorporating uncertainty in a layered HMM architecture for human activity recognition. In: Proceedings of the Joint Workshop on Human Gesture and Behavior Understanding (J-HGBU), pp. 33-34. ACM (2011)
8. Glodek, M., Scherer, S., Schwenker, F.: Conditioned hidden markov model fusion for multimodal classification. In: Proceedings of the Annual Conference of the International Speech Communication Association (ISCA), Interspeech, pp. 2269-2272 (2011)
9. Koller, D., Friedman, N.: Probabilistic graphical models: Principles and techniques. The MIT Press (2009)
10. Moore, D.J., Essa, I.A., Hayes III., M.H.: Exploiting human actions and object context for recognition tasks. In: Proceedings of the Seventh International Conference on Computer Vision, vol. 1, pp. 80-86. IEEE (1999)
11. Morency, L.P., Quattoni, A., Darrell, T.: Latent-dynamic discriminative models for continuous gesture recognition. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-8. IEEE (2007)
12. Nguyen, N.T., Phung, D.Q., Venkatesh, S., Bui, H.: Learning and detecting activities from movement trajectories using the hierarchical hidden Markov models. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 955-960. IEEE (2005)
13. Oliver, N., Garg, A., Horvitz, E.: Layered representations for learning and inferring office activity from multiple sensory channels. Special Issue on Event Detection in Video: Computer Vision and Image Understanding 96(2), 163-180 (2004)
14. Park, S., Aggarwal, J.K.: A hierarchical Bayesian network for event recognition of human actions and interactions. Journal of Multimedia Systems 10(2), 164-179 (2004)
15. Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers 10(3), 61-74 (1999)
16. Schölkopf, B., Smola, A.J., Williamson, R.C., Bartlett, P.L.: New support vector algorithms. Journal of Neural computation 12(5), 1207-1245 (2000)
17. Sung, J., Ponce, C., Selman, B., Saxena, A.: Human activity detection from RGBD images. In: Proceedings of the AAAI Workshop on Pattern, Activity and Intent Recognition (2011)
18. Turaga, P., Chellappa, R., Subrahmanian, V.S., Udrea, O.: Machine recognition of human activities: A survey. IEEE Transactions on Circuits and Systems for Video Technology 18(11), 1473-1488 (2008)
19. Yu, C., Ballard, D.H.: A multimodal learning interface for grounding spoken language in sensory perceptions. ACM Transactions on Applied Perception 1(1), 57-80 (2004)