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2017 | OriginalPaper | Chapter

Abnormal Crowd Behavior Detection Based on Combined Approach of Energy Model and Threshold

Authors : Madhura Halbe, Vibha Vyas, Yogita M. Vaidya

Published in: Pattern Recognition and Machine Intelligence

Publisher: Springer International Publishing

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Abstract

The world population continues to grow. The size of gatherings at various venues under different circumstances is tremendously increasing. Any mass assembly has potential risk of the lethal crowd disaster. The various algorithms in computer vision techniques are being developed as a part of proactive system for crowd management. This paper presents novel approach to identify abnormal crowd behavior. The algorithm employedoptical flow method to estimate displacement vectors of moving crowd and computation of crowd motion energy. The crowd motion energy was further modified by crowd motion intensity (CMI). The peaks in the CMI characteristics were the indicators of abnormal activity and were detected more accurately by applying threshold. The algorithm has been tested on standard UMN dataset with an average accuracy of 91.66 percentages. The accuracy has been improved with application of threshold. Adaptive threshold may aid to further improve the performance of algorithm.

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Metadata
Title
Abnormal Crowd Behavior Detection Based on Combined Approach of Energy Model and Threshold
Authors
Madhura Halbe
Vibha Vyas
Yogita M. Vaidya
Copyright Year
2017
Book
Pattern Recognition and Machine Intelligence

Print ISBN: 978-3-319-69899-1

Electronic ISBN: 978-3-319-69900-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-69900-4_24

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