TY - JOUR A2 - Park, Dong S. AU - Zhang, Shugang AU - Wei, Zhiqiang AU - Nie, Jie AU - Huang, Lei AU - Wang, Shuang AU - Li, Zhen PY - 2017 DA - 2017/07/20 TI - A Review on Human Activity Recognition Using Vision-Based Method SP - 3090343 VL - 2017 AB - 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. SN - 2040-2295 UR - https://doi.org/10.1155/2017/3090343 DO - 10.1155/2017/3090343 JF - Journal of Healthcare Engineering PB - Hindawi KW - ER -