A Review on Human Activity Recognition Using Vision-Based Method

Abstract EN

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.