eprintid: 17417
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/74/17
datestamp: 2023-12-19 03:23:48
lastmod: 2023-12-19 03:23:48
status_changed: 2023-12-19 03:08:01
type: article
metadata_visibility: show
creators_name: Farooq, M.U.
creators_name: Mohamad Saad, M.N.
creators_name: Daud Khan, S.
creators_name: Saleh, Y.
title: Anomaly Localization at High-Density Crowd Using Motion Shape Image (MSI)
ispublished: pub
keywords: Anomaly; Anomaly localizations; Divergence; FTLE; LCS; Localisation; Localised; Motion shape image; Point detection; Start point, Feature extraction
note: cited By 0; Conference of 1st International Conference on Artificial Intelligence for Smart Community, AISC 2020 ; Conference Date: 17 December 2020 Through 18 December 2020; Conference Code:286319
abstract: Anomaly localization plays a critical role if a disaster occurs in a high-density crowd to efficiently rescue the crowd from the right location. This paper enriches anomaly localization by introducing new localization features, in addition to the �only� localization feature of source/start point detection existing literature offers. New features introduced include crowd density estimation in localized regions and direction/angle of a localized region. A motion shape image (MSI) based approach is introduced for localization and features detection and experimentation is performed on benchmark and our proposed high-density crowd datasets. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
date: 2022
publisher: Springer Science and Business Media Deutschland GmbH
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142684569&doi=10.1007%2f978-981-16-2183-3_84&partnerID=40&md5=d3ada1a94ec67eec21d02a8ffe24264d
id_number: 10.1007/978-981-16-2183-3₈₄
full_text_status: none
publication: Lecture Notes in Electrical Engineering
volume: 758
pagerange: 889-906
refereed: TRUE
isbn: 9789811621826
issn: 18761100
citation:   Farooq, M.U. and Mohamad Saad, M.N. and Daud Khan, S. and Saleh, Y.  (2022) Anomaly Localization at High-Density Crowd Using Motion Shape Image (MSI).  Lecture Notes in Electrical Engineering, 758.  pp. 889-906.  ISSN 18761100