A Review on Video-Based Human Activity Recognition

By Shian-Ru Ke, Le Uyen Thuc Hoang, Yong-Jin Lee, Jenq-Neng Hwang, Jang-Hee Yoo, Kyoung-Ho Choi. 2013

435 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion (2019, neutral): “However, visual data affect the privacy of inhabitants, which calls for privacy preserving approaches in videos and images [54, 63-65].”
Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, background): “Several earlier surveys were also published to review methods to recognize human poses, motions, gestures, and activities [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], as well as their applications [49], [50].”
Joint Prediction of Activity Labels and Starting Times in Untrimmed Videos (2017, background): “We would like to refer to article [17] and [31] for a comprehensive review of the state-of-the-art approaches.”
Skeleton-based Human Activity Recognition for Video Surveillance (2015, background): “It is a doubly stochastic process [25].”
Action Recognition Using Rate-Invariant Analysis of Skeletal Shape Trajectories (2016, background): “This is due to its huge potential in a wide spectrum of areas, such as human-machine interaction, psychology [3], surveillance security, healthcare and social assistance, video games, and so on.”