Abstract
In the field of human computer interaction (HCI) the detection and classification of human activity patterns has become an important challenge. The problem can be understood as a specific problem of pattern recognition which addresses three topics, namely fusion of multiple modalities, spatio-temporal structures and a vast variety of pattern appearances the more abstract a pattern gets. In order to approach the problem, we propose a layered architecture which decomposes temporal patterns into elementary sub-patterns. Within each layer the patterns are detected using Markov models. The results of a layer are passed to the next successive layer such that on each layer the temporal granularity and the complexity of patterns increases. A dataset containing activities in an office scenario was recorded. The activities are decomposed to basic actions which are detected on the first layer. We evaluated a two-layered architecture using the dataset showing the feasibility of the approach.
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References
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)
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)
Dietrich, C., Palm, G., Riede, K., Schwenker, F.: Classification of bioacoustic time series based on the combination of global and local decisions. Pattern Recognition 37(12), 2293–2305 (2004)
Ekman, P.: An argument for basic emotions. Cognition & Emotion 6(3-4), 169–200 (1992)
Ekman, P.: Facial expression and emotion. American Psychologist 48, 384–392 (1993)
Freeman, W., Roth, M.: Orientation histograms for hand gesture recognition. Tech. Rep. TR94-03, Mitsubishi Electrical Research Laboratories (1995); originally published at the International Workshop on Automatic Face and Gesture Recognition
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)
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 (Interspeech), pp. 2269–2272. ISCA (2011)
Jaeger, H.: Short term memory in echo state networks. Tech. Rep. GMD Report 152, Fraunhofer Institute for Autonomous Intelligent Systems (2002)
Koller, D., Friedman, N.: Probabilistic graphical models: Principles and techniques. The MIT Press (2009)
Littlewort, G., Whitehill, J., Wu, T., Fasel, I., Frank, M., Movellan, J., Bartlett, M.: The computer expression recognition toolbox (CERT). In: Proceedings of the International Conference on Automatic Face & Gesture Recognition and Workshops, pp. 298–305. IEEE (2011)
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)
Park, S., Aggarwal, J.: A hierarchical Bayesian network for event recognition of human actions and interactions. Journal of Multimedia Systems 10(2), 164–179 (2004)
Platt, J.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers 10(3), 61–74 (1999)
Schölkopf, B., Smola, A., Williamson, R., Bartlett, P.: New support vector algorithms. Journal of Neural Computation 12(5), 1207–1245 (2000)
Schuller, B., Rigoll, G., Lang, M.: Hidden Markov model-based speech emotion recognition. In: Proceedings of the International Conference on Multimedia and Expo (ICME), vol. 2, pp. 401–404. IEEE (2003)
Sung, J., Ponce, C., Selman, B., Saxena, A.: Unstructured human activity detection from RGBD images. In: Proceedings of the International Conference on Robotics and Automation (ICRA), pp. 842–849. IEEE (2012)
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Glodek, M., Layher, G., Schwenker, F., Palm, G. (2012). Recognizing Human Activities Using a Layered Markov Architecture. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_85
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DOI: https://doi.org/10.1007/978-3-642-33269-2_85
Publisher Name: Springer, Berlin, Heidelberg
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