Abstract
Hidden Markov models have been extensively and successfully used for the recognition of human actions. Though there exist well-established algorithms to optimize the transition and output probabilities, the type of features to use and specifically the number of states and Gaussian have to be chosen manually. Here we present a quantitative study on selecting the optimal feature set for recognition of simple object manipulation actions pointing, rotating and grasping in a table-top scenario. This study has resulted in recognition rate higher than 90%. Also three different parameters, namely the number of states and Gaussian for HMM and the number of training iterations, are considered for optimization of the recognition rate with 5 different feature sets on our motion capture data set from 10 persons.
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References
Aggarwal, J.K., Cai, Q.: Human motion analysis: A review. Computer Vision and Image Understanding: CVIU 73(3), 428–440 (1999)
Ascension. Motion Star Real-Time Motion Capture. http://www.ascension-tech.com/products/motionstar_10_04.pdf
Billard, A., Calinon, S., Guenter, F.: Discriminative and adaptative imitation in uni-manual and bi-manual tasks. Robotics and Autonomous Systems 54, 370–384 (2006)
Calinon, S., Billard, A., Guenter, F.: Discriminative and adaptative imitation in uni-manual and bi-manual tasks. Robotics and Autonomous Systems 54 (2005)
Grest, D., Koch, R., Krueger, V.: Single view motion tracking by depth and silhoutte information. In: Scandinavian Conference on Image Analysis (2007)
Guenter, S., Bunke, H.: Optimizing the number of states and training iterations and gaussians in an hmm-based handwritten word recognizer. In: Seventh International Conference on Document Analysis and Recognition, vol. 1, pp. 472–476 (August 2003)
Mataric, M.J.: Sensory-motor primitives as a basis for imitation: linking perception to action and biology to robotics. In: Dautenhahn, K., Nehaniv, C.L. (eds.) Imitation in Animals and Artifacts, pp. 391–422. MIT Press, Cambridge (2002)
Jenkins, O.C., Mataric, M.J.: Performance-derived behavior vocabularies: Data-driven acqusition of skills from motion. International Journal of Humanoid Robotics 1(2), 237–288 (2004)
Krueger, V., Grest, D.: Using hidden markov models for recognizing action primitives in complex actions. In: Scandinavian Conference on Image Analysis (2007)
Campbell, L., Bobick, A.: Recognition of human body motion using phase space constraints. In: International Conference in Computer Vision, pp. 624–630 (1995)
Moeslund, T., Hilton, A., Krueger, V.: A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 104, 90–126 (2006)
Murphy, K.: Hidden Markov Model (HMM) Toolbox for Matlab (1998), http://www.cs.ubc.ca/~murphyk/Software/HMM/hmm.html
Newtson, D., Engquist, G., Bois, J.: The objective basis of behavior unit. Journal of Personality and social psychology, 847–862 (1977)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Vicente, I.S., Kragic, D.: Learning and recognition of object manipulation actions using linear and nonlinear dimensionality reduction. In: 15th IEEE Int. Symp. on Robot and Human Interactive Communication (RO-MAN) (submitted, 2007)
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Raamana, P.R., Grest, D., Krueger, V. (2007). Human Action Recognition in Table-Top Scenarios : An HMM-Based Analysis to Optimize the Performance. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds) Computer Analysis of Images and Patterns. CAIP 2007. Lecture Notes in Computer Science, vol 4673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74272-2_13
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DOI: https://doi.org/10.1007/978-3-540-74272-2_13
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