@article{scholars17417,
         journal = {Lecture Notes in Electrical Engineering},
       publisher = {Springer Science and Business Media Deutschland GmbH},
           pages = {889--906},
            year = {2022},
           title = {Anomaly Localization at High-Density Crowd Using Motion Shape Image (MSI)},
            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},
          volume = {758},
             doi = {10.1007/978-981-16-2183-3{$_8$}{$_4$}},
        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 {\^a}??only{\^a}?? 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. {\^A}{\copyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142684569&doi=10.1007\%2f978-981-16-2183-3\%5f84&partnerID=40&md5=d3ada1a94ec67eec21d02a8ffe24264d},
        keywords = {Anomaly; Anomaly localizations; Divergence; FTLE; LCS; Localisation; Localised; Motion shape image; Point detection; Start point, Feature extraction},
            isbn = {9789811621826},
          author = {Farooq, M. U. and Mohamad Saad, M. N. and Daud Khan, S. and Saleh, Y.},
            issn = {18761100}
}