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Computers 2013, 2(2), 88-131; doi:10.3390/computers2020088

A Review on Video-Based Human Activity Recognition

1
Department of Electrical Engineering, University of Washington, Seattle, WA 98195-2500, USA
2
Department of ETE, Danang University of Technology, Danang, Vietnam
3
Video Surveillance Research Section, ETRI, 305-700 Daejeon , Korea
4
Department of Information & Electronics Engineering, Mokpo National University, Jeollanam-do 534-729, Korea
*
Author to whom correspondence should be addressed.
Received: 29 November 2012 / Revised: 21 February 2013 / Accepted: 30 April 2013 / Published: 5 June 2013
(This article belongs to the Special Issue Activity Detection and Novel Sensing Technologies)

Abstract

This review article surveys extensively the current progresses made toward video-based human activity recognition. Three aspects for human activity recognition are addressed including core technology, human activity recognition systems, and applications from low-level to high-level representation. In the core technology, three critical processing stages are thoroughly discussed mainly: human object segmentation, feature extraction and representation, activity detection and classification algorithms. In the human activity recognition systems, three main types are mentioned, including single person activity recognition, multiple people interaction and crowd behavior, and abnormal activity recognition. Finally the domains of applications are discussed in detail, specifically, on surveillance environments, entertainment environments and healthcare systems. Our survey, which aims to provide a comprehensive state-of-the-art review of the field, also addresses several challenges associated with these systems and applications. Moreover, in this survey, various applications are discussed in great detail, specifically, a survey on the applications in healthcare monitoring systems.
Keywords: human activity recognition; segmentation; feature representation; security surveillance; healthcare monitoring; human computer interface human activity recognition; segmentation; feature representation; security surveillance; healthcare monitoring; human computer interface
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Ke, S.-R.; Thuc, H.L.U.; Lee, Y.-J.; Hwang, J.-N.; Yoo, J.-H.; Choi, K.-H. A Review on Video-Based Human Activity Recognition. Computers 2013, 2, 88-131.

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