%P 889-906 %I Springer Science and Business Media Deutschland GmbH %A M.U. Farooq %A M.N. Mohamad Saad %A S. Daud Khan %A Y. Saleh %V 758 %T Anomaly Localization at High-Density Crowd Using Motion Shape Image (MSI) %L scholars17417 %J Lecture Notes in Electrical Engineering %O 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 %R 10.1007/978-981-16-2183-3₈₄ %D 2022 %K Anomaly; Anomaly localizations; Divergence; FTLE; LCS; Localisation; Localised; Motion shape image; Point detection; Start point, Feature extraction %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 â��onlyâ�� 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.