Fuzzy qualitative human motion analysis

This chapter proposes a fuzzy qualitative (FQ) 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 FQ robot kinematics with human motion tracking and recognition algorithms. First, a data quantisation process is proposed to relax the computational complexity suffered from visual tracking algorithms. Secondly, a novel human motion representation, Qualitative Normalised Template (QNT), is developed in terms of the FQ robot kinematics framework to effectively represent human motion. The human skeleton is modelled as a complex kinematic chain, 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 Hidden Markov Model (FHMM) shows that the proposed approach consistently outperforms both hidden Markov models in human motion recognition.