%0 Journal Article
%@ 18761100
%A Farooq, M.U.
%A Mohamad Saad, M.N.
%A Daud Khan, S.
%A Saleh, Y.
%D 2022
%F scholars:17417
%I Springer Science and Business Media Deutschland GmbH
%J Lecture Notes in Electrical Engineering
%K Anomaly; Anomaly localizations; Divergence; FTLE; LCS; Localisation; Localised; Motion shape image; Point detection; Start point, Feature extraction
%P 889-906
%R 10.1007/978-981-16-2183-3₈₄
%T Anomaly Localization at High-Density Crowd Using Motion Shape Image (MSI)
%U https://khub.utp.edu.my/scholars/17417/
%V 758
%X 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.
%Z 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