A Review on Computer Vision-Based Methods for Human Action Recognition

Journal of Imaging

David Ndzi¹  John Chiverton²  Mahmoud Al-Faris²  AhmedIsam Ahmed² 

[1] School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK;School of Energy & Electronic Engineering, Faculty of Technology, University of Portsmouth, Portsmouth PO1 3DJ, UK;

关键词: human action recognition;    hand-crafted feature;    deep learning;    feature representation;   

DOI  :  10.3390/jimaging6060046

来源: DOAJ

【 摘 要 】

Human action recognition targets recognising different actions from a sequence of observations and different environmental conditions. A wide different applications is applicable to vision based action recognition research. This can include video surveillance, tracking, health care, and human–computer interaction. However, accurate and effective vision based recognition systems continue to be a big challenging area of research in the field of computer vision. This review introduces the most recent human action recognition systems and provides the advances of state-of-the-art methods. To this end, the direction of this research is sorted out from hand-crafted representation based methods including holistic and local representation methods with various sources of data, to a deep learning technology including discriminative and generative models and multi-modality based methods. Next, the most common datasets of human action recognition are presented. This review introduces several analyses, comparisons and recommendations that help to find out the direction of future research.

【 授权许可】

Unknown