Abstract
The objective of human action recognition is to recognize and comprehend human behaviour in videos with the help of expertly matched tags. Thus, there are a plethora of applications for human action recognition, such as video surveillance and patient monitoring. In this paper, three methods are developed and implemented to address these challenging tasks. Convolutional neural networks (CNNs) are the basis for the CNN+LSTM, 3D CNN, and TwoStream CNN algorithms, which are used to recognize human actions in videos.
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