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Dr. Vijayan Asari, Director

Phone: 937-229-4504
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Vision Lab

Action Recognition

Action Recognition Based on Multi-Level Representation of a Space Time Shape

This approach considers human actions as space time shapes which is a concatenation of silhouettes, and uses a combination of shape descriptors to extract features suitable for recognition. The current algorithm uses a 3D Euclidean distance transform as one shape descriptor, the purpose of which is to give interior values in accordance to the shape boundary which is different for different human actions. By using the values obtained, the space time shape is dissected inwards to get coarser and coarser representation. At each level of the coarser representation, the R-Transform and R-Translation which is a variant of the Radon transform, is extracted and these provide the action features. R-Transform gives the variation of the posture of the human silhouette at every instant of the space time shape while the R-Translation discriminates between actions which involves a large translation of the body with those which does not. The figure below gives the flow diagram of the algorithm along with sample space time shapes of jumping jack and walking.

Jumping JackFlowchartWalking

Jumping Jack ST                                                                                                                                         Walking ST

Distance Transform Values

Distance Transform Values

Gradient of the Distance Transform

Gradient of the Distance Transform

Segmentation of the Space Time Shape

Segmentation of the Space Time Shape

R-Transform Feature 

Jumping JackJumping Jack

WalkWalk


Video Demonstration

Action Recognition

(Research Demonstrated at Old Dominion University Vision Lab)

Segmentation for Human Activity Recognition