Human Action Recognition in Table-Top Scenarios : An HMM-Based Analysis to Optimize the Performance

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.

[1]  Jake K. Aggarwal,et al.  Human Motion Analysis: A Review , 1999, Comput. Vis. Image Underst..

[2]  Jake K. Aggarwal,et al.  Human motion analysis: a review , 1997, Proceedings IEEE Nonrigid and Articulated Motion Workshop.

[3]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[4]  Daniel Grest,et al.  Using Hidden Markov Models for Recognizing Action Primitives in Complex Actions , 2007, SCIA.

[5]  Reinhard Koch,et al.  Single View Motion Tracking by Depth and Silhouette Information , 2007, SCIA.

[6]  Aude Billard,et al.  Discriminative and adaptive imitation in uni-manual and bi-manual tasks , 2006, Robotics Auton. Syst..

[7]  Maja J. Mataric,et al.  Performance-Derived Behavior Vocabularies: Data-Driven Acquisition of Skills from Motion , 2004, Int. J. Humanoid Robotics.

[8]  Maja J. Matarić,et al.  Sensory-motor primitives as a basis for imitation: linking perception to action and biology to robotics , 2002 .

[9]  Darren Newtson,et al.  The objective basis of behavior units. , 1977 .

[10]  Aaron F. Bobick,et al.  Recognition of human body motion using phase space constraints , 1995, Proceedings of IEEE International Conference on Computer Vision.

[11]  K. Dautenhahn,et al.  Imitation in Animals and Artifacts , 2002 .

[12]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[13]  Danica Kragic,et al.  Learning and Recognition of Object Manipulation Actions Using Linear and Nonlinear Dimensionality Reduction , 2007, RO-MAN 2007 - The 16th IEEE International Symposium on Robot and Human Interactive Communication.

[14]  Horst Bunke,et al.  Optimizing the number of states, training iterations and Gaussians in an HMM-based handwritten word recognizer , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..