relation: https://khub.utp.edu.my/scholars/17392/
title: Deep Learning Approach for Divergence Behavior Detection at High Density Crowd
creator: Farooq, M.U.
creator: Mohamad Saad, M.N.
creator: Saleh, Y.
creator: Daud Khan, S.
description: At high-density crowd gatherings, people naturally escape from the region where any unexpected event happens. Escape in high-density crowds appears as a divergence pattern in the scene and timely detecting divergence patterns can save many human lives. In this paper, we propose to physically capture crowd normal and divergence motion patterns (or motion shapes) in form of images and train a shallow convolution neural network (CNN) on motion shape images for divergence behavior detection. Crowd motion pattern shape is obtained by extracting ridges of Lagrangian Coherent Structure (LCS) from the Finite-Time Lyapunov Exponent (FTLE) field and convert ridges into the grey-scale image. We also propose a divergence localization algorithm to pinpoint anomaly location(s). Experimentation is carried out on synthetic crowd datasets simulating normal and divergence behaviors at the high-density crowd. Comparison with state-of-the-art methods shows our method can obtain better accuracy for both divergence behavior detection and localization problems. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2022
type: Article
type: PeerReviewed
identifier:   Farooq, M.U. and Mohamad Saad, M.N. and Saleh, Y. and Daud Khan, S.  (2022) Deep Learning Approach for Divergence Behavior Detection at High Density Crowd.  Lecture Notes in Electrical Engineering, 758.  pp. 875-888.  ISSN 18761100     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142720874&doi=10.1007%2f978-981-16-2183-3_83&partnerID=40&md5=9c5c7d43a72376018a4bea17f89f3ae6
relation: 10.1007/978-981-16-2183-3₈₃
identifier: 10.1007/978-981-16-2183-3₈₃