A Review on Human Activity Recognition Using Vision-Based Method

• Published in: Journal of healthcare engineering Vol. 2017; no. 2017; pp. 1 - 31
• Main Authors: Wang, Shuang, Huang, Lei, Nie, Jie, Wei, Zhiqiang, Zhang, Shugang, Li, Zhen
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2017; Hindawi; John Wiley & Sons, Inc
• Subjects: Human Activities; Humans; Pattern Recognition, Automated; Review; Vision, Ocular
• ISSN: 2040-2295; 2040-2309
• DOI: 10.1155/2017/3090343

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.