Published Paper Details:

In this research work, we proposed a most effective noble approach for Human action recognition in real-time environments. We recognize several distinct dynamic human activity actions using kinect. A 3D skeleton data is processed from real-time video gesture to sequence of frames and getter skeleton joints (Energy Joints), orientation, rotations of joint angles from random selected number of frames. Thus after extracting the set of frames we implements classification techniques Principal Component Analysis (PCA), Artificial Neural Network (ANN) and Deep Neural Network (DNN) respectively with some variants for classify our all different gesture models. However we conclude that use very less number of frame (10-15%) for train our system efficiently from entire set of gesture frames. After successfully completion of our classification methods we got an excellent overall accuracy 94%, 96% and 98% respectively. We finally observe that our proposed system is more useful then comparing to other existing system, therefore this model is best suitable for real-time application such as in video games for player action/gesture recognition.