Fuzzy qualitative human motion analysis
Document Type
Article
Publication Date
1-1-2009
Abstract
This paper proposes a fuzzy qualitative approach to vision-based human motion analysis with an emphasis on human motion recognition. It achieves feasible computational cost for human motion recognition by combining fuzzy qualitative robot kinematics with human motion tracking and recognition algorithms. First, a data-quantization process is proposed to relax the computational complexity suffered from visual tracking algorithms. Second, a novel human motion representation, i.e., qualitative normalized template, is developed in terms of the fuzzy qualitative robot kinematics framework to effectively represent human motion. The human skeleton is modeled as a complex kinematic chain, and its motion is represented by a series of such models in terms of time. Finally, experiment results are provided to demonstrate the effectiveness of the proposed method. An empirical comparison with conventional hidden Markov model (HMM) and fuzzy HMM (FHMM) shows that the proposed approach consistently outperforms both HMMs in human motion recognition.
Keywords
computational complexity, data-quantization process, fuzzy HMM, fuzzy qualitative human motion analysis, fuzzy Motion recognition, human motion tracking, vision-based human motion analysis, visual tracking algorithms
Divisions
ai
Publication Title
IEEE Transactions on Fuzzy Systems
Volume
17
Issue
4
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Additional Information
Chan, Chee Seng Liu, Honghai