Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention
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 a...
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2023
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| Online Access: | https://hdl.handle.net/11073/32529 |
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| _version_ | 1864513432873598976 |
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| author | Nazir, Amril |
| author2 | Mitra, Rohan Sulieman, Hana Kamalov, Firuz |
| author2_role | author author author |
| author_facet | Nazir, Amril Mitra, Rohan Sulieman, Hana Kamalov, Firuz |
| author_role | author |
| dc.creator.none.fl_str_mv | Nazir, Amril Mitra, Rohan Sulieman, Hana Kamalov, Firuz |
| dc.date.none.fl_str_mv | 2023 2025-12-08T07:05:01Z 2025-12-08T07:05:01Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Nazir, A.; Mitra, R.; Sulieman, H.; Kamalov, F. Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention. Sensors 2023, 23, 5811. https://doi.org/10.3390/s23135811 1424-8220 https://hdl.handle.net/11073/32529 10.3390/s23135811 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | MDPI |
| dc.relation.none.fl_str_mv | https://doi.org/10.3390/s23135811 |
| dc.subject.none.fl_str_mv | Automated crime detection Suspicious behavior Crime prevention Temporal features Time-series classification |
| dc.title.none.fl_str_mv | Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention |
| dc.type.none.fl_str_mv | Peer-Reviewed Published version info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | 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. |
| format | article |
| id | aus_47b1d567a1bf6d61d062fb744c059703 |
| identifier_str_mv | Nazir, A.; Mitra, R.; Sulieman, H.; Kamalov, F. Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention. Sensors 2023, 23, 5811. https://doi.org/10.3390/s23135811 1424-8220 10.3390/s23135811 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/32529 |
| publishDate | 2023 |
| publisher.none.fl_str_mv | MDPI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime PreventionNazir, AmrilMitra, RohanSulieman, HanaKamalov, FiruzAutomated crime detectionSuspicious behaviorCrime preventionTemporal featuresTime-series classificationThe 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.American University of SharjahZayed UniversityMDPI2025-12-08T07:05:01Z2025-12-08T07:05:01Z2023Peer-ReviewedPublished versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfNazir, A.; Mitra, R.; Sulieman, H.; Kamalov, F. Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention. Sensors 2023, 23, 5811. https://doi.org/10.3390/s231358111424-8220https://hdl.handle.net/11073/3252910.3390/s23135811en_UShttps://doi.org/10.3390/s23135811oai:repository.aus.edu:11073/325292025-12-08T11:21:28Z |
| spellingShingle | Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention Nazir, Amril Automated crime detection Suspicious behavior Crime prevention Temporal features Time-series classification |
| status_str | publishedVersion |
| title | Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention |
| title_full | Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention |
| title_fullStr | Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention |
| title_full_unstemmed | Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention |
| title_short | Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention |
| title_sort | Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention |
| topic | Automated crime detection Suspicious behavior Crime prevention Temporal features Time-series classification |
| url | https://hdl.handle.net/11073/32529 |