Abstract
Dynamic Time Warping (DTW) is commonly used in gesture recognition tasks in order to tackle the temporal length variability of gestures. In the DTW framework, a set of gesture patterns are compared one by one to a maybe infinite test sequence, and a query gesture category is recognized if a warping cost below a certain threshold is found within the test sequence. Nevertheless, either taking one single sample per gesture category or a set of isolated samples may not encode the variability of such gesture category. In this paper, a probability-based DTW for gesture recognition is proposed. Different samples of the same gesture pattern obtained from RGB-Depth data are used to build a Gaussian-based probabilistic model of the gesture. Finally, the cost of DTW has been adapted accordingly to the new model. The proposed approach is tested in a challenging scenario, showing better performance of the probability-based DTW in comparison to state-of-the-art approaches for gesture recognition on RGB-D data.
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References
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A comparison of affine region detectors. International Journal of Computer Vision 65(1/2), 43–72 (2005)
ChaLearn Gesture Dataset (CGD 2011), ChaLearn, California, Copyright (c) ChaLearn - 2011 (2011)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech and Signal Processing 26(1), 43–49 (1978)
Reyes, M., Dominguez, G., Escalera, S.: Feature weighting in dynamic time warping for gesture recognition in depth data. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1182–1188 (2011)
Zhou, F., la Torre, F.D., Hodgins, J.K.: Hierarchical aligned cluster analysis for temporal clustering of human motion. IEEE Transaction on Pattern Analysis and Machine Intelligence 35(3), 582–596 (2010)
Lv, F., Nevatia, R.: Single View Human Action Recognition using Key Pose Matching and Viterbi Path Searching. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8, 17–22 (2007)
Svensen, M., Bishop, C.M.: Robust bayesian mixture modelling. In: Proceedings of European Symposium on Artificial Neural Networks, vol. 64, pp. 235–252 (2005)
Hampapur, A., Brown, L., Connell, J., Ekin, A., Haas, N., Lu, M., Merkl, H., Pankanti, S.: Smart video surveillance: exploring the concept of multiscale spatiotemporal tracking. IEEE Signal Processing Magazine 22(2), 38–51 (2005)
Pentland, A.: Socially aware computation and communication. Computer 38, 33–40 (2005)
Starner, T., Pentland, A.: Real-time American Sign Language recognition from video using hidden Markov models. In: Proceedings of the International Symposium on Computer Vision, pp. 265–270 (1995)
Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A., Cook, M., Moore, R.: Real-time human pose recognition in parts from single depth images. Communications of the ACM 56, 116–124 (2013)
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Bautista, M.Á. et al. (2013). Probability-Based Dynamic Time Warping for Gesture Recognition on RGB-D Data. In: Jiang, X., Bellon, O.R.P., Goldgof, D., Oishi, T. (eds) Advances in Depth Image Analysis and Applications. WDIA 2012. Lecture Notes in Computer Science, vol 7854. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40303-3_14
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DOI: https://doi.org/10.1007/978-3-642-40303-3_14
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