The objective of human action recognition is to develop algorithms and models that can accurately identify and classify human actions based on video. The aim is to enable machines to understand and interpret human actions, which can have a wide range of applications in various fields, such as healthcare, sports, security, and robotics.
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 personβs 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 are proposing a deep learning-based Convolution Neural Network (CNN) algorithm and OpenCV that which can train the dataset and recognize the human actions/activities.
Keywords: Human action/activity recognition, deep learning, CNN, OpenCV
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