Advances in human action recognition: an updated survey

• Published in: IET image processing Vol. 13; no. 13; pp. 2381 - 2394
• Main Author: Abu-Bakar, Syed A.R
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 14.11.2019
• Subjects: basic process flow; categorisation; categorise; classifier‐type; computer vision; descriptor‐type; feature extraction; features‐type; HAR; human action recognition; human activity recognition; image motion analysis; Image Processing communities; image recognition; input data‐type; learning (artificial intelligence); numerous publications; Review Article; review papers; two‐layer hierarchical approach; updated survey; classifier-type; basic process flow; human action recognition; two-layer hierarchical approach; features-type; human activity recognition; categorise; categorisation; image motion analysis; input data-type; feature extraction; computer vision; HAR; review papers; descriptor-type; learning (artificial intelligence); updated survey; Image Processing communities; numerous publications; image recognition
• ISSN: 1751-9659; 1751-9667
• DOI: 10.1049/iet-ipr.2019.0350

Research in human activity recognition (HAR) has seen tremendous growth and continuously receiving attention from both the Computer Vision and the Image Processing communities. Due to the existence of numerous publications in this field, undoubtedly, there have been a number of review papers on this subject that categorise these techniques. Many of the recent works have started to tackle more challenging problems and these proposed techniques are addressing more realistic real-world scenarios. Conspicuously, an updated survey that covers these methods is timely due. To simplify the categorisation, this study takes a two-layer hierarchical approach. At the top level, the categorisation is based on the basic process flow of HAR, i.e. input data-type, features-type, descriptor-type, and classifier-type. At the second layer, each of these components is further subcategorised based on the diversity of the proposed methods. Finally, a remark on the coming popularity of deep learning approach in this field is also given.