Detection and identification of human actions using Predictive Modular Neural Networks
Vassilios Petridis, Briti Deb, Vassilis Syrris · 2009
The aim of the present study is to validate a 2D kinematic model of human body in providing considerable features that they could be used for human actions classification. Human motion can be termed as a non-rigid, articulated motion, with body parts being piecewise rigid, held together by joints. The presented approach uses the fact that the human body has certain anthropometric proportion and uses the anatomical shape representation of the non-rigid and articulated human body contour. The body joints and the different body parts are detected with help of prior anatomical knowledge and extracted silhouette. The result of this kinematics based approach is a simple 2D human stick figure. Features are extracted from this 2D model and used to represent the human body. In the training phase, each training video is represented by a neural network, while in classification phase, the predictive modular neural network (PREMONN) time series classification algorithm is applied to classify the human actions.