Implementation of human action recognition using image parsing techniques

Human activity recognition plays a significant role in human-to-human interaction and interpersonal relations. Because it provides information about the identity of a person, their personality, and psychological state, it is difficult to extract. The human ability to recognize another person's activities is one of the main subjects of study of the scientific areas of computer vision and machine learning. As a result of this research, many applications, including video surveillance systems, human-computer interaction, and robotics for human behavior characterization, require a multiple activity recognition system. In image and video analysis, human activity recognition is an important research direction. In the past, a large number of papers have been published on human activity recognition in video and image sequences. In this paper, we provide a comprehensive survey of the recent development of the techniques, including methods, systems, and quantitative evaluation of the performance of human activity recognition. The experimental results show that our method can significantly improve classification, interpretation, and retrieval performance for the video images. The novelty of this paper is twofold. First, to capture the video images of human. Secondly, to identify the different types of action performed by human.