RETRACTED ARTICLE: The impact of criminal psychology trend prediction based on deep learning algorithm and three-dimensional convolutional neural network

Yi-lan, WU

doi:10.1007/s12652-021-03455-8

Cited by 8 publications

(2 citation statements)

References 45 publications

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“…Wu Y. et al [4] proposed a three-dimensional convolutional neural network (3D-CNN) to extract information features from continuous multiframe cube data and acquire the features of spatial-temporal dimensions. A three-dimensional CNN was proposed to represent time-series information on continuous multiframe cube data.…”

Section: Related Workmentioning

confidence: 99%

“…Several state-of-the-art techniques for preventing shoplifting have been proposed, including those by Kirichenko L. et al [1], Gandapur et al [2], Qin Z. et al [3], and Wu Y. et al [4]. These techniques heavily rely on CNN-based feature extraction methods that use pretrained models to extract visual appearance and optical flow features in order to represent spatiotemporal information.…”

Section: Introductionmentioning

confidence: 99%

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Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention

Nazir

Mitra

Sulieman

et al. 2023

Sensors

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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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Section: Related Workmentioning

confidence: 99%