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Estimation of Human Behaviors Based on Human Actions Using an ANN
Maimaitimin Maierdan,Keigo Watanabe,Shoichi Maeyama 제어로봇시스템학회 2014 제어로봇시스템학회 국제학술대회 논문집 Vol.2014 No.10
An approach to human behavior recognition is presented in this paper. The system is separated into two parts: human action recognition and object recognition. The estimation result is composed of a simple action “Pointing” and a virtual assumed object, which has two attributes, one is “current status” and the other is “acceptable behavior”. Once the human action and object are recognized, then detect whether a vector calculated by human elbow intersected the object. If the vector is intersected, then estimate human behavior by combining the human action and the object attribute. The artificial neural network (ANN) is discussed as a main part of the current research. Whole ANN processing is simulated by Octave 3.8, the human actions are captured by Microsoft Kinect, and a human model is built by using human joint data.
Human Behavior Recognition System Based on 3-dimensional Clustering Methods
Maimaitimin Maierdan,Keigo Watanabe,Shoichi Maeyama 제어로봇시스템학회 2013 제어로봇시스템학회 국제학술대회 논문집 Vol.2013 No.10
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.