3D skeletal gesture recognition through discriminative coding: Human activity recognition using time warping invariant Riemannian trajectories

To recognize the human activity is considered very essential in human-to-human interaction and interpersonal relations due to its nature of providing information regarding the identity of a people, their individuality, and psychological state. The extraction of this information is very challenging. The extraction of this information is very challenging. The major subject of study of the scientific areas of computer vision and ML is the potential of human for identifying the activities of another person. A sequence of human body movements in which different body parts are engaged in a concurrent manner is known as action. According to computer vision perception, any kind of observation is matched with earlier defined patterns and then it is labeled while recognizing the action. In this research work, the 3 D Skelton-based technique is proposed for human activity reorganization. The proposed technique will improve performance for the human activity detection in terms of accuracy, precision and recall also the proposed technique will compared with the other techniques which authenticates reliability of the model.