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A review on classifying abnormal behavior in crowd scene

Afiq, A.A. and Zakariya, M.A. and Saad, M.N. and Nurfarzana, A.A. and Khir, M.H.M. and Fadzil, A.F. and Jale, A. and Gunawan, W. and Izuddin, Z.A.A. and Faizari, M. (2019) A review on classifying abnormal behavior in crowd scene. Journal of Visual Communication and Image Representation, 58 . pp. 285-303.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Crowd behavior analysis has become one of the new areas of interest in the computer vision community due to the increasing demands from surveillance and security industries. It is important to meticulously understand crowd behavior to prevent any disaster and unwanted incidents such as thief, stampede and riots. For this purpose, crowd features such as density, motion and trajectory are analyzed to detect any abnormality in the crowd. Thus, this review is aimed to provide insight on several detection methods including Gaussian Mixture Model (GMM), Hidden Markov Model (HMM), Optical Flow method and Spatio-Temporal Technique (STT). Providing the latest development, the review presented the studies that are published in journals and conferences over the past 5 years. © 2018 Elsevier Inc.

Item Type:Article
Impact Factor:cited By 0
Uncontrolled Keywords:Disaster prevention; Gaussian distribution; Hidden Markov models; Image segmentation; Network security; Optical flows; Trellis codes, Abnormal detection; Crowd analysis; Crowd behavior analysis; Gaussian Mixture Model; Latest development; Optical flow methods; Spatio-temporal techniques; Vision communities, Behavioral research
ID Code:22211
Deposited By: Ahmad Suhairi
Deposited On:28 Feb 2019 05:06
Last Modified:28 Feb 2019 05:06

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