A Review on Human Activity Recognition Using Vision-Based Method

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Abstract

Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, and human-computer interaction (HCI). This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity representation and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods. Next, representative and available datasets are introduced. Aiming to provide an overview of those methods and a convenient way of comparing them, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the datasets they used. Finally, we investigate the directions for future research.

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Zhang, S., Wei, Z., Nie, J., Huang, L., Wang, S., & Li, Z. (2017). A Review on Human Activity Recognition Using Vision-Based Method. Journal of Healthcare Engineering. Hindawi Limited. https://doi.org/10.1155/2017/3090343

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