Hierarchical Self-organizing Maps System for Action Classification

We present a novel action recognition system that is able to learn how to recognize and classify actions. Our system employs a three-layered hierarchy of Self-Organizing Maps together with a supervised neural network for labelling the actions. We have evaluated our system in an experiments consisting of ten different actions from a publicly available data set. The results are encouraging with 83% correctly classified actions based on the actor’s spatial trajectory.

[1]  Wanqing Li,et al.  Action recognition based on a bag of 3D points , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[2]  Christian Balkenius,et al.  Ikaros: Building cognitive models for robots , 2010, Adv. Eng. Informatics.

[3]  Xiaodong Yang,et al.  EigenJoints-based action recognition using Naïve-Bayes-Nearest-Neighbor , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[4]  E. Rosch.  Cognitive Representations of Semantic Categories. , 1975 .

[5]  Majid Nili Ahmadabadi,et al.  Attention control learning in the decision space using state estimation , 2016, Int. J. Syst. Sci..

[6]  Ying Wu,et al.  Robust 3D Action Recognition with Random Occupancy Patterns , 2012, ECCV.

[7]  Marco La Cascia,et al.  Gesture Modeling by Hanklet-Based Hidden Markov Model , 2014, ACCV.

[8]  W. Frawley.  Mind as Action , 1998, Trends in Cognitive Sciences.

[9]  L. Vaina.  From shapes and movements to objects and actions , 2004, Synthese.

[10]  Majid Nili Ahmadabadi,et al.  Biologically Inspired Framework for Learning and Abstract Representation of Attention Control , 2008, WAPCV.

[11]  Jake K. Aggarwal,et al.  View invariant human action recognition using histograms of 3D joints , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[12]  S. Runeson,et al.  Kinematic specification of dynamics as an informational basis for person and action perception: Expe , 1983 .

[13]  G. Johansson.  Visual perception of biological motion and a model for its analysis , 1973 .

[14]  Peter Gärdenfors,et al.  Using Conceptual Spaces to Model Actions and Events , 2012, J. Semant..

[15]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[16]  Magnus Johnsson,et al.  Internal Simulation of an Agent's Intentions , 2012, BICA.

[17]  Magnus Johnsson,et al.  Hierarchies of Self-Organizing Maps for action recognition , 2016, Cognitive Systems Research.

[18]  D. Marr,et al.  Representation and recognition of the movements of shapes , 1982, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[19]  D. Marr,et al.  Representation and recognition of the spatial organization of three-dimensional shapes , 1978, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[20]  Peter Gärdenfors,et al.  Conceptual spaces - the geometry of thought , 2000 .

[21]  Zicheng Liu,et al.  HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Peter Gärdenfors,et al.  Representing actions and functional properties in conceptual spaces , 2007 .

[23]  Peter Gärdenfors,et al.  Action Recognition Online with Hierarchical Self-Organizing Maps , 2016, 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS).

[24]  Ying Wu,et al.  Mining actionlet ensemble for action recognition with depth cameras , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  John J. Craig Zhu,et al.  Introduction to robotics mechanics and control , 1991 .

[26]  Joseph J. LaViola,et al.  Exploring the Trade-off Between Accuracy and Observational Latency in Action Recognition , 2013, International Journal of Computer Vision.

[27]  G. Hesslow.  Conscious thought as simulation of behaviour and perception , 2002, Trends in Cognitive Sciences.

[28]  Christian Balkenius,et al.  Associative Self-organizing Map , 2009, IJCCI.

[29]  Magnus Johnsson,et al.  Recognizing actions with the associative self-organizing map , 2013, 2013 XXIV International Conference on Information, Communication and Automation Technologies (ICAT).

[30]  Magnus Johnsson,et al.  Discriminating and simulating actions with the associative self-organising map , 2015, Connect. Sci..

[31]  Magnus Johnsson,et al.  Simulating Actions with the Associative Self-Organizing Map , 2013, AIC@AI*IA.

[32]  Xiaodong Yang,et al.  Recognizing actions using depth motion maps-based histograms of oriented gradients , 2012, ACM Multimedia.

[33]  Magnus Johnsson,et al.  Action Recognition based on Hierarchical Self-Organizing Maps , 2014, AIC.