Human behavior recognition system based on 3-dimensional clustering methods

In this paper, a Hidden Markov Model (HMM) approach is introduced for recognizing human behaviors. Two main points are discussed in this approach: first is the application of HMM to the recognition system of human behaviors, and second is the effectiveness comparison of K-means and fuzzy C-means clustering algorithms. Three sample human behaviors are defined and the corresponded 3D data are collected using the Microsoft Kinect sensor (3D sensor). During these processing, we discuss the difference of k-means and fuzzy c-means clustering algorithms, and also we can see the results impacted by different clustering algorithms, the effectiveness of both clustering methods is shown through demonstrating the performance of our recognition system with HMM.