Abstract
We introduce a feature extraction scheme from a biologically inspired model using receptive fields (RFs) to point-light human motion patterns to form an action descriptor. The Echo State Network (ESN) which also has a biological plausibility is chosen for classification. We demonstrate the efficiency and robustness of applying the proposed feature extraction technique with ESN by constraining the test data based on arbitrary untrained viewpoints, in combination with unseen subjects under the following conditions: (i) lower sub-sampling frame rates to simulate data sequence loss, (ii) remove key points to simulate occlusion, and (iii) include untrained movements such as drunkard’s walk.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
CMU Graphics Lab: CMU Motion Capture Database. http://mocap.cs.cmu.edu/
Du, Y., Wang, W., Wang, L.: Hierarchical recurrent neural network for skeleton based action recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015
Jaeger, H.: Long Short-Term Memory in Echo State Networks: Details of a Simulation Study. Technical Report, Jacobs University, Bremen, Germany, February 2012
Jaeger, H., Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless telecommunication. Science 304(5667), 78–80 (2004)
Johansson, G.: Visual perception of biological motion and a model for its analysis. Percept. Psychophys. 14(2), 201–211 (1973)
Junejo, I.N., Dexter, E., Laptev, I., Perez, P.: View-independent action recognition from temporal self-similarities. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 172–185 (2011)
Kale, G.V., Patil, V.H.: A study of vision based human motion recognition and analysis. IJACI 7(2), 75–92 (2016)
Livne, M., Sigal, L., Troje, N.F., Fleet, D.J.: Human attributes from 3D pose tracking. Comput. Vision Image Underst. (CVIU) (2012)
Miller, L.E., Saygin, A.P.: Individual differences in the perception of biological motion: Links to social cognition and motor imagery. Cognition 128(2), 140–148 (2013)
Pascanu, R., Jaeger, H.: A neurodynamical model for working memory. Neural Netw. 24(2), 199–207 (2011)
Rautaray, S.S., Agrawal, A.: Vision based hand gesture recognition for human computer interaction: a survey. Artif. Intell. Rev. 43(1), 71 (2015)
Tanisaro, P., Schöning, J., Kurzhals, K., Heidemann, G., Weiskopf, D.: Visual analytics for video applications. Inf. Technol. 57, 30–36 (2015)
Tanisaro, P., Heidemann, G.: Time series classification using time warping invariant echo state networks. In: 15th IEEE International Conference on Machine Learning and Applications (ICMLA) (2016)
Tanisaro, P., Mahner, F., Heidemann, G.: Quasi view-independent human motion recognition in subspaces. In: Proceedings of 9th International Conference on Machine Learning and Computing (ICMLC) (2017)
Troje, N.F., Sadr, J., Geyer, H., Nakayama, K.: Adaptation aftereffects in the perception of gender from biological motion. J. Vision 6(8), 7 (2006)
Zhu, W., Lan, C., Xing, J., Zeng, W., Li, Y., Shen, L., Xie, X.: Co-occurrence feature learning for skeleton based action recognition using regularized deep lstm networks. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp. 3697–3703 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Tanisaro, P., Lehman, C., Sütfeld, L., Pipa, G., Heidemann, G. (2017). Classifying Bio-Inspired Model of Point-Light Human Motion Using Echo State Networks. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10613. Springer, Cham. https://doi.org/10.1007/978-3-319-68600-4_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-68600-4_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68599-1
Online ISBN: 978-3-319-68600-4
eBook Packages: Computer ScienceComputer Science (R0)