In this chapter we will discuss two problems related to action recognition: The first problem is the one of identifying in a surveillance surveillance scenario to determine walk or run gait and approximate direction. The second problem is concerned with the recovery of action primitives from observed complex actions. Both problems will be discussed within a statistical framework. Bayesian propagation over time offers a framework to treat likelihood observations at each time step and the dynamics between the time steps in a unified manner. The first problem will be approached as a pattern recognition and tracking task by a Bayesian propagation of the likelihoods. The latter problem will be approached by explicitly specifying the dynamics while the likelihood measure will estimate how well each dynamical model fits each time step. Extensive experimental results show the applicability of the Bayesian framework for action recognition and round up our discussion.
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References
A. Billard, Y. Epars, S. Calinon, S. Schaal, and G. Cheng. Discovering Optimal Imitation Strategies. Robotics and Autonomous Systems, 47:69-77, 2004.
A. Bobick. Movement, Activity, and Action: The Role of Knowledge in the Perception of Motion. Philosophical Trans. Royal Soc. London, 352:1257-1265, 1997.
A. Bobick. Movements, Activity, and Action: The Role of Knowledge in the Perception of Motion. In Royal Society Workshop on Knowledge-based Vision in Man and Machine, London, February 1997.
A. Bobick and J. Davis. The Recognition of Human Movement Using Temporal Templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23 (3):257-267, 2001.
H. Bourlard and N. Morgan. Connectionist Speech Recognition: A Hybrid Ap- proach. Kluwer Press, 1994.
G. Bradski and J. Davis. Motion Segmentation and Pose Recognition with Motion History Gradients. Machine Vision and Applications, 13, 2002.
S. Calinon and A. Billard. Stochastic Gesture Production and Recognition Model for a Humanoid Robot. In International Conference on Intelligent Robots and Systems, Alberta, Canada, Aug 2-6, 2005.
S. Calinon, F. Guenter, and A. Billard. Goal-Directed Imitation in a Humanoid Robot. In International Conference on Robotics and Automation, Barcelona, Spain, April 18-22, 2005.
D. Comaniciu, V. Ramesh, and P. Meer. Real-time Tracking of Non-rigid Ob- jects Using mean Shift. In Computer Vision and Pattern Recognition, volume 2, pages 142-149, Hilton Head Island, South Carolina, June 13-15, 2000.
N. Cuntoor and R. Chellappa. Key Frame-Based Activity Representation Using Antieigenvalues. In Asian Conference on Computer Vision, volume 3852 of LNCS, Hyderabad, India, Jan, 13-16, 2006.
B. Dariush. Human Motion Analysis for Biomechanics and Biomedicine. Ma-chine Vision and Applications, 14:202-205, 2003.
A. Doucet, S. Godsill, and C. Andrieu. On Sequential Monte Carlo Sampling Methods for Bayesian Filtering. Statistics and Computing, 10:197-209, 2000.
A. Efros, A. Berg, G. Mori, and J. Malik. Recognizing Action at a Distance. In Internatinal Conference on Computer Vision, Nice, France, Oct 13-16, 2003.
A. Elgammal and L. Davis. Probabilistic Framework for Segmenting People Under Occlusion. In ICCV, ICCV01, 2001.
A. Elgammal and C. Lee. Separating Style and Content on a Nonlinear Manifold. In Computer Vision and Pattern Recognition, Washington DC, June 2004.
A. Elgammal, V. Shet, Y. Yacoob, and L. Davis. Learning Dynamics for Exemplar-based Gesture Recognition. In Computer Vision and Pattern Recog-nition, Madison, Wisconsin, June 16-22, 2003.
M. Giese and T. Poggio. Neural Mechanisms for the Recognition of Biological Movements. Nature Reviews, 4:179-192, 2003.
H. Hermansky. Perceptual Linear Predictive (plp) Analysis of Speech. Journal of Acoustical Society of America, 87(4):1738-1725, 1990.
.X. Huang, Y. Ariki, and M. Jack. Hidden Markov Models for Speech Recognition. Edinburgh University Press, 1990.
X. Huang and M. Jack. Semi-continous Hidden Markov Models for Speech Signals. Computer Speech and Language, 3:239-252, 1989.
A. Ijspeert, J. Nakanishi, and S. Schaal. Movement Imitation withNonlinear Dy-namical Systems in Humanoid Robots. In International Conference on Robotics and Automation, Washington DC, May, 2002.
M. Isard and A. Blake. Condensation - Conditional Density Propagation for Visual Tracking. International Journal of Computer Vision, 29:5-28, 1998.
Y. Ivanov and A. Bobick. Recognition of Visual Activities and Interactions by Stochastic Parsing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):852-872, 2000.
O. Jenkins and M. Mataric. Deriving Action and Behavior Primitives from Human Motion Capture Data. In International Conference on Robotics and Automation, Washington DC, May 2002.
O. Jenkins and M. Mataric. Deriving Action and Behavior Primitives from Human Motion Data. In International Conference on Intelligent Robots and Systems, pages 2551-2556, Lausanne, Switzerland, Sept 30-Oct 4, 2002.
A. Kale, A. Sundaresan, A. Rjagopalan, N. Cuntoor, A. Chowdhury, V. Krueger, and R. Chellappa. Identification of Humans Using Gait. IEEE Transactions on Image Processing, 9:1163-1173, 2004.
G. Kitagawa. Monta Carlo Filter and Smoother for Non-gaussian Nonlinear State Space Models. Journal of Computational and Graphical Statistics, 5:1-25, 1996.
V. Krueger, J. Anderson, and T. Prehn. Probabilistic Model-based Background Subtraction. In Scandinavian Conference on Image Analysis,, pages 180-187, June 19-22, Joensuu, Finland, 2005.
V. Krueger, J. Anderson, and T. Prehn. Probabilistic Model-based Background Subtraction. In International Conference on Image Analysis and Processing, pages 180-187, Sept. 6-8, Cagliari, Italy, 2005.
B. Li and R. Chellappa. Simultanious Tracking and Verification via Sequential Posterior Estimation. In Computer Vision and Pattern Recognition, Hilton Head Island, South Carolina, June 13-15, 2000.
J. Liu and R. Chen. Sequential Monte Carlo for Dynamic Systems. Journal of the American Statistical Association, 93:1031-1041, 1998.
C. Lu and N. Ferrier. Repetitive Motion Analysis: Segmentation and Event Classification. IEEE Transactions on Pattern Analysis and Machine Intelli-gence, 26(2):258-263, 2004.
D. MacKay. In M. Jordan, editor, Learning in Graphical Models, Introduction to Monte Carlo Methods, pages 175-204. MIT Press, 1999.
O. Masound and N. Papanikolopoulos. A Method for Human Action Recogni-toin. Image and Vision Computing, 21:729-743, 2003.
H.-H. Nagel. From Image Sequences Towards Conceptual Descriptions. Image and Vision Computing, 6(2):59-74, 1988.
D. Ormoneit, H. Sidenbladh, M. Black, and T. Hastie. Learning and Track-ing Cyclic Human Motion. In Workshop on Human modelling, Analysis and Synthesis at CVPR, Hilton Head Island, South Carolina, June 13-15 2000.
L. R. Rabiner and B. H. Juang. An introduction to hidden Markov models. IEEE ASSP Magazine, pages 4-15, January 1986.
C. Rao, A. Yilmaz, and M. Shah. View-Invariant Representation and Recogni-tion of Actions. Journal of Computer Vision, 50(2), 2002.
L. Reng, T. Moeslund, and E. Granum. Finding Motion Primitives in Human Body Gestures. In S. Gibet, N. Courty, and J.-F. Kamps, editors, GW 2005, number 3881 in LNAI, pages 133-144. Springer, Berlin Heidelberg, 2006.
L. Reng, T. Moeslund, and E. Granum. Finding motion primitives in human body gestures. In S. Gibet, N. Courty, and J.-F. Kamp, editors, GW 2005, pages 133-144. Springer, 2006.
Y. Ricquebourg and P. Bouthemy. Real-Time Tracking of Moving Persons by Exploiting Spatio-Temporal Image Slices. Transactions on Pattern Analysis and Machine Intelligence, 22(8), 2000.
J. Rittscher, A. Blake, and S. Roberts. Towards the Automatic Analysis of Complex Human Body Motions. Image and Vision Computing, 20, 2002.
G. Rizzolatti, L. Fogassi, and V. Gallese. Parietal Cortex: From Sight to Action. Current Opinion in Neurobiology, 7:562-567, 1997.
G. Rizzolatti, L. Fogassi, and V. Gallese. Neurophysiological Mechanisms Un-derlying the Understanding and Imitation of Action. Nature Reviews, 2:661-670, Sept, 2001.
M. Roh, B. Christmas, J. Kittler, and S. Lee. Robust Player Gesture Spotting and Recognition in Low-Resolution Sports Video. In European Conference on Computer Vision, Graz, Austria, May 7-13, 2006.
S. Schaal. Is Imitation Learning the Route to Humanoid Robots? Trends in Cognitive Sciences, 3(6):233-242, 1999.
A. Stolcke. An Efficient Probabilistic Context-Free Parsing Algorithm That Computes Prefix Probabilities. Computational Linguistics, 21(2):165-201, 1995.
J. Tenenbaum, V. de Silva, and J. Langford. A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science, 290:2319-2323, 2000.
D. Vecchio, R. Murray, and P. Perona. Decomposition of Human Motiom into Dynamics-based Primitives with Application to Drawing Tasks. Automatica, 39,2003.
S. Zhou, V. Krueger, and R. Chellappa. Probabilistic Recognition of Human Faces From Video. IJCV, 91:214-245, July, 2003.
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Krüger, V. (2008). Recognition of Action as a Bayesian Parameter Estimation Problem over Time. In: Rosenhahn, B., Klette, R., Metaxas, D. (eds) Human Motion. Computational Imaging and Vision, vol 36. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6693-1_3
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