{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:16:08Z","timestamp":1776442568728,"version":"3.51.2"},"reference-count":33,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T00:00:00Z","timestamp":1687392000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"American University of Sharjah and Zayed University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The rise in crime rates in many parts of the world, coupled with advancements in computer vision, has increased the need for automated crime detection services. To address this issue, we propose a new approach for detecting suspicious behavior as a means of preventing shoplifting. Existing methods are based on the use of convolutional neural networks that rely on extracting spatial features from pixel values. In contrast, our proposed method employs object detection based on YOLOv5 with Deep Sort to track people through a video, using the resulting bounding box coordinates as temporal features. The extracted temporal features are then modeled as a time-series classification problem. The proposed method was tested on the popular UCF Crime dataset, and benchmarked against the current state-of-the-art robust temporal feature magnitude (RTFM) method, which relies on the Inflated 3D ConvNet (I3D) preprocessing method. Our results demonstrate an impressive 8.45-fold increase in detection inference speed compared to the state-of-the-art RTFM, along with an F1 score of 92%,outperforming RTFM by 3%. Furthermore, our method achieved these results without requiring expensive data augmentation or image feature extraction.<\/jats:p>","DOI":"10.3390\/s23135811","type":"journal-article","created":{"date-parts":[[2023,6,23]],"date-time":"2023-06-23T02:34:07Z","timestamp":1687487647000},"page":"5811","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7116-7607","authenticated-orcid":false,"given":"Amril","family":"Nazir","sequence":"first","affiliation":[{"name":"College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8007-5437","authenticated-orcid":false,"given":"Rohan","family":"Mitra","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, American University of Sharjah, Sharjah, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4952-8298","authenticated-orcid":false,"given":"Hana","family":"Sulieman","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, American University of Sharjah, Sharjah, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Firuz","family":"Kamalov","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Canadian University Dubai, Dubai, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kirichenko, L., Radivilova, T., Sydorenko, B., and Yakovlev, S. 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