Abstract
Humanity’s recognition action from a visual standpoint content is a difficult task as different types of problems arise in the recognition of human action. In the realm of computer vision, human action recognition (HAR) has reached a significant milestone. The advancement of technology allows us to address this issue and makes it a viable topic of research. A lot of research has already been done on HAR, and still, a lot is left. In this context, the focus of this survey is on the various types of HAR approaches that have been developed in the recent ten years. This paper uses a hidden Markov model based on several algorithms to solve the problem of human action recognition. We are comparing two techniques of HAR to find the best out of them. The hidden Markov model and the convolution neural network are two types of neural network. A convolutional neural network (CNN) capable of recognizing local patterns in input data is trained to recognize human actions from the local patterns in the feature representation. We found out that CNN is a better algorithm for recognizing human actions as it shows the body’s movement and body joints in two different aspects of the CNN graph.
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Ahmed, A., Jain, G., Sharma, A., Hashim, M., Raj, A. (2023). A Comprehensive Survey on Visualization of Human Action Recognition: By Hidden Markov Model and Convolution Neural Network. In: Sharma, R., Kannojiya, R., Garg, N., Gautam, S.S. (eds) Advances in Engineering Design. FLAME 2022. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-99-3033-3_17
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DOI: https://doi.org/10.1007/978-981-99-3033-3_17
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