3D human action recognition using model segmentation

This paper addresses a learning-based human action recognition system from multiple images based on integrating features of segmented 3D human body parts such as face, torso, and limbs. The innovation of our proposed 3D human action recognition system consists of three parts: (1) 3D reconstruction of the target object by tracking the position of a target object in a scene to voxelize the accurate 3D human model, (2) Human body model segmentation into several human body parts using ellipsoidal models in the space of second-order three dimensional diffusion tensor fields, and (3) Classification and recognition of human actions from features of the segmented human model using Multiple-Kernel based Support Vector Machine. Experimental results on a set of test volume data show that our proposed method is very efficient to visualize and recognize the human action using few parameters which are independent to partial occlusion, dimension, and viewpoint.