Abstract
In this paper, we take a bag of visual words approach to investigate whether it is possible to distinguish conversational scenarios from observing human motion alone, in particular gestures in 3D. The conversational interactions concerned in this work have rather subtle differences among them. Unlike typical action or event recognition, each interaction in our case contain many instances of primitive motions and actions, many of which are shared among different conversation scenarios. Hence, extracting and learning temporal dynamics are essential. We adopt Kinect sensors to extract low level temporal features. These features are then generalized to form a visual vocabulary that can be further generalized to a set of topics from temporal distributions of visual vocabulary. A subject-specific supervised learning approach based on both generative and discriminative classifiers is employed to classify the testing sequences to seven different conversational scenarios. We believe this is among one of the first work that is devoted to conversational interaction classification using 3D pose features and to show this task is indeed possible.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64
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References
Aggarwal, J.K., Ryoo, M.S.: Human activity analysis: A review. ACM Comput. Surv. 43(3), 16 (2011)
Yao, A., Gall, J., Fanelli, G., Gool, L.V.: Does human action recognition benefit from pose estimation? In: Proceedings of the British Machine Vision Conference, pp. 67.1–67.11. BMVA Press (2011)
Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)
Fang, H., Deng, J., Xie, X., Grant, P.W.: From clamped local shape models to global shape model. In: Proceedings of the 2013 International Conference on Image Processing, ICIP (2013)
Fathi, A.: Social interactions: A first-person perspective. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), CVPR 2012, pp. 1226–1233. IEEE Computer Society, Washington, DC (2012), http://dl.acm.org/citation.cfm?id=2354409.2354936
Gee, A.H., Cipolla, R.: Determining the gaze of faces in images. Image and Vision Computing 12, 639–647 (1994)
Holte, M.B., Tran, C., Trivedi, M.M., Moeslund, T.B.: Human pose estimation and activity recognition from multi-view videos: Comparative explorations of recent developments. IEEE Journal of Selected Topics in Signal Processing 6(5), 538–552 (2012)
Hospedales, T., Gong, S., Xiang, T.: Video behaviour mining using a dynamic topic model. International Journal of Computer Vision, 1–21 (2012)
Ivanov, Y.A., Bobick, A.F.: Recognition of visual activities and interactions by stochastic parsing. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 852–872 (2000)
Kovar, L., Gleicher, M.: Automated extraction and parameterization of motions in large data sets. ACM Trans. Graph. 23(3), 559–568 (2004)
Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 104(2-3), 90–126 (2006)
Müller, M., Röder, T., Clausen, M.: Efficient content-based retrieval of motion capture data. ACM Trans. Graph. 24(3), 677–685 (2005)
Niebles, J., Wang, H., Fei-Fei, L.: Unsupervised learning of human action categories using spatial-temporal words. International Journal of Computer Vision 79(3), 299–318 (2008)
Oliver, N., Garg, A., Horvitz, E.: Layered representations for learning and inferring office activity from multiple sensory channels. Comput. Vis. Image Underst. 96(2), 163–180 (2004), http://dx.doi.org/10.1016/j.cviu.2004.02.004
Oliver, N., Rosario, B., Pentland, A.: A bayesian computer vision system for modeling human interactions. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 831–843 (2000)
Ryoo, M.S., Aggarwal, J.K.: Semantic representation and recognition of continued and recursive human activities. Int. J. Comput. Vision 82(1), 1–24 (2009)
Ryoo, M.S., Aggarwal, J.K.: Stochastic representation and recognition of high-level group activities. Int. J. Comput. Vision 93(2), 183–200 (2011)
Turaga, P.K., Chellappa, R., Subrahmanian, V.S., Udrea, O.: Machine recognition of human activities: A survey. IEEE Trans. Circuits Syst. Video Techn. 18(11), 1473–1488 (2008)
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Deng, J., Xie, X., Daubney, B., Fang, H., Grant, P.W. (2013). Recognizing Conversational Interaction Based on 3D Human Pose. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_13
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DOI: https://doi.org/10.1007/978-3-319-02895-8_13
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