Abstract
It is vital to combat crimes by predicting and detecting the occurrence of crime, especially in urban cities. Hence this study proposed investigating the capability of six deep learning models, namely the AlexNet, GoogleNet, ResNet18, ResNet50, ResNet101 and InceptionV3, in determining the most optimum model for snatch theft detection. Two categories of databases comprising 13000 images of snatch theft and non-snatch activities were generated from 120 videos obtained from the Google and YouTube platforms. These images are further used for training and testing these six DL models, along with data augmentation implemented during training to avoid overfitting. However, it was found that overfitting occurred based on training and testing accuracy plots, and hence, it was decided to re-train the model using an early stopping method. Thus, upon completion of re-training all six models, it was found that all six models showed a good-fit condition, with ResNet 50 attaining the highest testing accuracy of 98.9% and 100% sensitivity. As for specificity, ResNet 101 showed the highest value, precisely 97.7%.
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Acknowledgment
This research was funded by the Ministry of Higher Education (MOHE) Malaysia, Grant No: 600-IRMI/FRGS 5/3 (394/2019), Sponsorship File No: FRGS/1/2019/ TK04/UITM/01/3. The authors would like to thank the College of Engineering, Universiti Teknologi MARA (UiTM), Shah Alam, Selangor, Malaysia for the facilities provided in this research.
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Zamri, N.F.M., Tahir, N.M., Ali, M.S.A.M., Ashar, N.D.K. (2023). Snatch Theft Detection Using Deep Learning Models. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-18461-1_17
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