Due to concerns like backdrop cluttering, incomplete obstruction, scale disparities, viewpoint, illumination, and appearance, identifying activities of humans from a sequence of video or still photos are a complex issue. Multiple movement recognition structures is necessary for numerous applications, such as a video investigation mechanism, human-computer interface (HCI), and robotics for characterising human behaviour. In this work, we bestow a comprehensive assessment of recent and advanced designs involved in the classification of human activity. We outline a classification of human activity approaches and go through their benefits and drawbacks. Specifically, we classify human activities categorization approaches into two broad classes based on if or not they make use of information from several modes. Next, each of these classes is broken down into its subclasses, which illustrate how each category models human activity.
Human Activity Recognition, Machine Learning, Computer Vision
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