Abstract
This paper presents a gesture recognition method for detecting and classifying both cyclic and non-cyclic human motion patterns in real-time applications. The semantic segmentation of a constantly captured human motion data stream is a key research topic, especially if both cyclic and non-cyclic gestures are considered during the human-computer interaction. The system measures the temporal coherence of the movements being captured according to its knowledge database, and once it has a sufficient level of certainty on its observation semantics the motion pattern is labeled automatically. In this way, our recognition method is also capable of handling time-varying dynamic gestures. The effectiveness of the proposed method is demonstrated via recognition experiments with a triple-axis accelerometer and a 3D tracker used by various performers.
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References
Mitra, S., Acharya, T.: Gesture Recognition: A Survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 37(3), 311–324 (2007)
Jaimes, A., Sebe, N.: Multimodal Human Computer Interaction: A Survey. Computer Vision and Image Understanding 108(1-2), 116–134 (2007)
Lösch, M., Schmidt-Rohr, S.R., Knoop, S., Dillmann, R.: Feature Selection for Human Activity Recognition Using Feature Taxonomies and User Comments. In: Proceedings of the International Conference on Cognitive Systems, Karlsruhe, Germany (2008)
Liu, G., Zhang, J., Wang, W., McMillan, L.: Human Motion Estimation from a Reduced Marker Set. In: Proceedings of the 2006 Symposium on Interactive 3D Graphics and Games, Boston, MA, USA (2006)
Masoud, O., Papanikolopoulos, N.: A Method for Human Action Recognition. Image and Vision Computing 21(8), 729–743 (2003)
Weinland, D., Ronfard, R., Boyer, E.: Motion History Volumes for Free Viewpoint Action Recognition. In: Proceedings of the Workshop on Modeling People and Human Interaction, Beijing, China, vol. 104, pp. 249–257 (2005)
Rahman, M.M., Robles-Kelly, A.: A Tuned Eigenspace Technique for Articulated Motion Recognition. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 174–185. Springer, Heidelberg (2006)
Ahmad, M., Lee, S.-W.: Human Action Recognition Using Multi-View Image Sequences Features. In: Proceedings of the International Conference on Automatic Face and Gesture Recognition, Southampton, UK, pp. 523–528 (2006)
Rittscher, J., Blake, A., Roberts, S.J.: Towards the Automatic Analysis of Complex Human Body Motions. Image and Vision Computing 20, 905–916 (2002)
Ren, H., Xu, G., Kee, S.: Subject-Independent Natural Action Recognition. In: Proceedings of the International Conference on Automatic Face and Gesture Recognition, Seoul, Korea, pp. 523–528 (2004)
Unzueta, L., Mena, O., Sierra, B., Suescun, Á.: Kinetic Pseudo-Energy History for Human Dynamic Gestures Recognition. In: Perales, F.J., Fisher, R.B. (eds.) AMDO 2008. LNCS, vol. 5098, pp. 390–399. Springer, Heidelberg (2008)
Mena, O., Unzueta, L., Sierra, B., Matey, L.: Temporal Nearest End-Effectors for Real-Time Full-Body Human Actions Recognition. In: Perales, F.J., Fisher, R.B. (eds.) AMDO 2008. LNCS, vol. 5098, pp. 269–278. Springer, Heidelberg (2008)
Kubota, N., Abe, M.: Computational Intelligence for Cyclic Gestures Recognition of A Partner Robot. In: Proceedings of the International Conference on Knowledge-Based Intelligent Information & Engineering Systems, Melbourne, Australia, pp. 650–656 (2005)
Yu, H., Sun, G.-m., Song, W.-x., Li, X.: Human Motion Recognition Based on Neural Network. In: Proceedings of the International Conference on Communications, Circuits and Systems, Hong Kong, China, vol. 2, p. 982 (2005)
Ramanan, D., Forsyth, D.A.: Automatic Annotation of Everyday Movements. In: Proceedings of the Neural Information Processing Systems Conference, Vancouver, BC, Canada (2003)
Guo, F., Qian, G.: Dance Posture Recognition Using Wide-Baseline Orthogonal Stereo Cameras. In: Proceedings of the International Conference on Automatic Face and Gesture Recognition, Tempe, AZ, USA, pp. 481–486 (2006)
Kang, H., Lee, C.W., Jung, K.: Recognition-Based Gesture Spotting in Video Games. Pattern Recognition Letters 25(15), 1701–1714 (2004)
Stiefmeier, T., Roggen, D.: Gestures Are Strings: Efficient Online Gesture Spotting and Classification Using String Matching. In: Proceedings of the International Conference on Body Area Networks, Florence, Italy (2007)
Stone, M.: Cross-Validation Choice and Assessment of Statistical Procedures. Journal of Royal Statistical Society 36, 111–147 (1974)
Unzueta, L.: Markerless Full-Body Human Motion Capture and Combined Motor Action Recognition for Human-Computer Interaction. PhD Thesis, Tecnun, University of Navarra, Donostia-San Sebastián, Spain (2009)
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Unzueta, L., Goenetxea, J. (2010). Cyclic and Non-cyclic Gesture Spotting and Classification in Real-Time Applications. In: Perales, F.J., Fisher, R.B. (eds) Articulated Motion and Deformable Objects. AMDO 2010. Lecture Notes in Computer Science, vol 6169. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14061-7_17
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DOI: https://doi.org/10.1007/978-3-642-14061-7_17
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