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Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention
Open accessAbstract
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.
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There is a real danger of AI becoming pervasive in every sense where those involved may be exposed to risks without being aware of them.
“There is a real danger of AI becoming pervasive in every sense where those involved may be exposed to risks without being aware of them [32,90].”
From New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution (2023) · cites this paper as [90]
The Results and Discussion section (page unavailable) identifies a risk from the proposed detection system: false positives could lead to unnecessary interventions or the detainment of innocent people. It does not address AI becoming pervasive or people being unaware of the risks they face. (LLM, conf=0.78)
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References 30 · Cited by 30
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Metadata
- Authors
- Nazir, A., Mitra, R., Sulieman, H., & Kamalov, F.
- Affiliations
- Zayed University (AE)
- American University of Sharjah (AE)
- Canadian University of Dubai (AE)
- Corresponding author
- Nazir, Amril
- Kamalov, Firuz
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2023-06-22
- Volume
- 23
- Issue
- 13
- Pages
- 5811
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC10347130
- OpenAlex
- W4381802977
- Cited by
- 30
- References
- 30
- Citations by year
- 2026: 4 · 2025: 16 · 2024: 9 · 2023: 1
- FWCI
- 2.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DSpace at AUS (American University of Sharjah) (submittedVersion)
- Funders
- American University of Sharjah and Zayed University
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Keywords
- suspicious activity detection, shoplifting prevention, temporal feature extraction, time series classification, YOLOv5, DeepSORT, human tracking, UCF-Crime dataset, inference speed
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