A Comprehensive Survey of Vision-Based Human Action Recognition Methods
Open Access
- 27 February 2019
- Vol. 19 (5), 1005
- https://doi.org/10.3390/s19051005
Abstract
Although widely used in many applications, accurate and efficient human action recognition remains a challenging area of research in the field of computer vision. Most recent surveys have focused on narrow problems such as human action recognition methods using depth data, 3D-skeleton data, still image data, spatiotemporal interest point-based methods, and human walking motion recognition. However, there has been no systematic survey of human action recognition. To this end, we present a thorough review of human action recognition methods and provide a comprehensive overview of recent approaches in human action recognition research, including progress in hand-designed action features in RGB and depth data, current deep learning-based action feature representation methods, advances in human–object interaction recognition methods, and the current prominent research topic of action detection methods. Finally, we present several analysis recommendations for researchers. This survey paper provides an essential reference for those interested in further research on human action recognition.Keywords
This publication has 83 references indexed in Scilit:
- Real-time human action recognition based on depth motion mapsJournal of Real-Time Image Processing, 2013
- Locating and recognizing multiple human actions by searching for maximum score subsequencesSignal, Image and Video Processing, 2013
- Representation Learning: A Review and New PerspectivesIEEE Transactions on Pattern Analysis and Machine Intelligence, 2013
- Real-time human pose recognition in parts from single depth imagesCommunications of the ACM, 2013
- Recognizing 50 human action categories of web videosMachine Vision and Applications, 2012
- A survey on activity recognition and behavior understanding in video surveillanceThe Visual Computer, 2012
- Selective spatio-temporal interest pointsComputer Vision and Image Understanding, 2012
- Human activity analysisACM Computing Surveys, 2011
- On Space-Time Interest PointsInternational Journal of Computer Vision, 2005
- The recognition of human movement using temporal templatesIEEE Transactions on Pattern Analysis and Machine Intelligence, 2001