Video-Based Abnormal Human Behavior Recognition—A Review

By M. I. Oluwatoyin P. Popoola, Ke-jun Wang. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews), 2012

511 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/15b35446c29c3d702f686f7aeb73e77da773416f

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Citations in the corpus

Fuzzy human motion analysis: A review (2014, background): “Amongst all, video surveillance is one of the most important real-time applications [6, 7, 8, 9, 10].”
A survey of video datasets for human action and activity recognition (2013, background): “Contents lists available at SciVerse ScienceDirect Computer Vision and Image Understanding journal homepage: www.elsevier .com/ locate/cviu A survey of video datasets for human action and activity recognition q Jose M. Chaquet a,⇑, Enrique J. Carmona a, Antonio Fernández-Caballero b a Dpto. de Intel”
Detecting anomalous events in videos by learning deep representations of appearance and motion (2017, background): “Existing techniques tackling the abnormal video event detection are extensively reviewed in [25].”
Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities (2019, background): “Collaborative drones and IoT technologies such as human behavior recognition [215],”
(2014). Online quality assessment of human movement from skeleton data . Paper presented at British Machine Vision Conference, (2014, background): “In particular, the discrimination of anomalies has been a strong focus, as illustrated by the comprehensive survey in [13].”
Learning Temporal Regularity in Video Sequences (2016, background): “The survey paper [6] contains a comprehensive review of this topic.”
Remembering history with convolutional LSTM for anomaly detection (2017, unknown): “We refer readers to [14] for a comprehensive review of hand-crafted features based methods.”
Crowded Scene Analysis: A Survey (2015, background): “Methods based on knowledge from physical systems for crowd representation are convenient to apply but their capacity is limited to recognize certain patterns [100].”
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos (2018, background): “A detailed review of abnormal human behavior and crowd motion analysis is provided in [17] and [18].”