A review on classifying abnormal behavior in crowd scene
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, stampe...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Published: |
2019
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058057075&doi=10.1016%2fj.jvcir.2018.11.035&partnerID=40&md5=d4386619b0e9dbdfb33d13c9d61505a8 http://eprints.utp.edu.my/22211/ |
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| Summary: | 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. |
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