Abstract
The research presented here makes a contribution to the understanding of the recognition of biological motion by comparing human recognition of a set of everyday gestures and motions with machine interpretation of the same dataset. Our reasoning is that analysis of any differences and/or correlations between the two could reveal insights into how humans themselves perceive motion and hint at the most important cues that artificial classifiers should be using to perform such a task. We captured biological motion data from human participants engaged in a number of everyday activities, such as walking, running and waving, and then built two artificial classifiers (a Finite State Machine and a multi-layer perceptron artificial neural network, ANN) which were capable of discriminating between activities. We then compared the accuracy of these classifiers with the abilities of a group of human observers to interpret the same activities when they were presented as moving light displays (MLDs). Our results suggest that machine recognition with ANNs is not only comparable to human levels of recognition but can exceed it in some instances.
Chapter PDF
Similar content being viewed by others
References
Johansson, G.: Visual perception of biological motion and a model for its analysis. Perception and Psychophysics 4(20), 201–211 (1973)
Gavrila, D.M.: The Visual Analysis of Human Movement: A Survey. Computer Vision and Image Understanding 73, 82–98 (1999)
Essa, I.A.: Computers Seeing People. AI Magazine 20, 69–82 (1999)
Laxmi, V., Carter, J.N., Damper, R.I.: Biologically-Inspired Human Motion Detection. In: Proc of ESANN - European Symposium on Artificial Neural Networks, Bruges, Belgium, 24-26, 2002, pp. 95–100 (2002)
Van Laerhoven, K., Aidoo, K.A., Lowette, S.: Real-time Analysis of Data from Many Sensors with Neural Networks. In: 5th IEEE International Symposium on Wearable Computers (ISWC) 2001, vol. 115 (2001)
Heidemann, G., Bekel, H., Bax, I., Saalbach, A.: Hand Gesture Recognition: Self-Organising Maps as a Graphical User Interface for the Partitioning of Large Training Data Sets, icpr. In: 17th International Conference on Pattern Recognition (ICPR 2004), 2004, vol. 4, pp. 487–490 (2004)
Rumelhart, D.E., McClelland, J.: Parallel Distributed Processing, vol. 1. MIT Press, Cambridge, MA (1986)
Corradini, A., Gross, M.: A Hybrid Stochastic-Connectionist Architecture for Gesture Recognition. In: International Conference on Information Intelligence and Systems 1999, p. 336 (1999)
Hong, P., Turk, M., Huang, T.S.: Constructing Finite State Machines for Fast Gesture Recognition. In: ICPR 2000: Proceedings of the International Conference on Pattern Recognition 2000, IEEE Computer Society, Los Alamitos (2000)
Hong, P., Turk, M., Huang, T.S.: Gesture Modelling and Recognition Using Finite State Machines. In: IEEE, Fourth International Conference on Automatic Face and Gesture Recognition 2000, pp. 28–30 (2000)
El Kaliouby, R., Robinson, P.: Real Time Head Gesture Recognition in Affective Interfaces. Human-Computer Interaction Journal 03, 950–953 (2003)
Lee, H., Kim, J.H.: An {HMM}-Based Threshold Model Approach for Gesture Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 961–973 (1999)
Emering, L., Boulic, R., Thalmann, D.: Interacting with Virtual Humans through Body Actions. IEEE, Computer Graphics and Applications 18, 8–11 (1998)
Madabhushi, A., Aggarwal, J.K.: A Bayesian Approach to Human Activity Recognition. In: Second IEEE Workshop on Visual Surveillance 1999, p. 25 (1999)
Sun, X., Chen, C., Manjunath, B.S.: Probabilistic Motion Parameter Models for Human Activity Recognition. In: Proceedings of IEEE International Conference on Pattern Recognition (ICPR) Québec City, Canada,2002 (2002)
Rose, R.C.: Discriminant Word Spotting Techniques for Rejection Non-Vocabulary Utterances in Unconstrained Speech. In: Proc. IEEE Int’l Conf. Acoustics, Speech, and Signal Processing, San Francisco, 1992, vol. II, pp. 105–108 (1992)
Takahashi, K., Seki, S., Oka, R.: Spotting Recognition of Human Gestures from Motion Images Technical Report IE92-134, The Inst, of Electronics, Information, and Comm. Engineers, Japan, pp. 9–16 (1992)
Baudel, T., Beaudouin-Lafon, M.: CHARADE: Remote Control of Objects Using Free-Hand Gestures. Commun. ACM 36(7), 28–35 (1993)
Nickel, K., Stiefelhagen, R.: Pointing gesture recognition based on 3D-tracking of face, hands and head orientation. In: 5th international conference on Multimodal interfaces, 2003, pp. 140–146 (2003)
Desolneux, A., Moisan, L., More, J.: Computational gestalts and perception thresholds. Journal of Physiology - Paris 97, 311–324 (2003)
Kozlowski, L.T., Cutting, J.E.: Recognising the gender of walkers from point-lights mounted on ankles: Some second thoughts. Perception & Psychophysics 23, 459 (1978)
Kozlowski, L.T., Cutting, J.E.: Recognising the sex of a walker from a dynamic point-light display. Perception & Psychophysics 21, 575–580 (1977)
Runeson, S., Frykholm, G.: Kinematic specifications of dynamics as an informational basis for person-and-action perception: Expectation, gender recognition, and deceptive intention. Jounal of Experimental Psychology, General 112, 585–615 (1983)
Vallortigara, G., Regolin, L., Marconato, F.: Attraction to Motion. PLoS Biol. 3(7) (2005)
Polhemus, Liberty (2003), http://www.polhemus.com/
Bishop, C.: Neural Networks for Pattern Recognition. University Press, Oxford (1995)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jones, T.D., Lawson, S.W., Benyon, D., Armitage, A. (2007). Comparison of Human and Machine Recognition of Everyday Human Actions. In: Duffy, V.G. (eds) Digital Human Modeling. ICDHM 2007. Lecture Notes in Computer Science, vol 4561. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73321-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-73321-8_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73318-8
Online ISBN: 978-3-540-73321-8
eBook Packages: Computer ScienceComputer Science (R0)