A Novel Human Action Recognition Approach based on 3D Tracked Body Joints and its Application
Date Issued
2011
Date
2011
Author(s)
You, Zong-Hua
Abstract
This study presents an innovative action recognition approach using tracked human body joint locations from a depth sensor with full-body tracking capability. The proposed method encodes actions in a weighted directed action graph to model the kinematics of actions and models distribution of joints to be a set of salient postures that correspond to the nodes in the action graph. In addition, we propose a hierarchical action seeking framework for increasing recognition performance and raising the accuracy rate. In our hierarchical framework, the human body is divided into four parts ( left upper limb, right upper limb, left lower limb, and right lower limb). We propose a motion indicator to evaluate the degree of movement in each human limb, referred to as motion descriptor. Then, the system classifies observed motion into several a specific action clusters using motion descriptor content. Second, we employ a smaller action data set, which is relative to specific motion, to seek the most appropriate action. Experimental results show that about 80% recognition accuracy were gained in non-hierarchical system, and over 90% recognition accuracy were achieved in hierarchical system.
Moreover, we employ the proposed action recognition framework to an interactive performance between an intelligent puppet and actors. In this performance, the puppet is able to realize the meaning of actor’s behavior by our action recognition framework. Thus, the puppet is able to make related action to the actors.
Experimental results demonstrate the proposed hierarchical-based action recognition approach reduces the computational cost and increases the accuracy rate.
Moreover, we employ the proposed action recognition framework to an interactive performance between an intelligent puppet and actors. In this performance, the puppet is able to realize the meaning of actor’s behavior by our action recognition framework. Thus, the puppet is able to make related action to the actors.
Experimental results demonstrate the proposed hierarchical-based action recognition approach reduces the computational cost and increases the accuracy rate.
Subjects
Action recognition
Action graph
Shape descriptor
Salient posture
Motion descriptor
Type
thesis
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