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Quantum Computing Based Neural Networks for Anomaly Classification in Real-Time Surveillance Videos

MD. Yasar Arafath1,*, A. Niranjil Kumar2

1 Department of Computer Science Engineering, Jairupaa College of Engineering Thottiapalayam, Tirupur District, 641604, Tamilnadu, India
2 Department of Electronics and Communications Engineering, Dhanalakshmi College of Engineering, Chennai, 600015, Tamilnadu, India

* Corresponding Author: MD. Yasar Arafath. Email: email

Computer Systems Science and Engineering 2023, 46(2), 2489-2508. https://doi.org/10.32604/csse.2023.035732

Abstract

For intelligent surveillance videos, anomaly detection is extremely important. Deep learning algorithms have been popular for evaluating real-time surveillance recordings, like traffic accidents, and criminal or unlawful incidents such as suicide attempts. Nevertheless, Deep learning methods for classification, like convolutional neural networks, necessitate a lot of computing power. Quantum computing is a branch of technology that solves abnormal and complex problems using quantum mechanics. As a result, the focus of this research is on developing a hybrid quantum computing model which is based on deep learning. This research develops a Quantum Computing-based Convolutional Neural Network (QC-CNN) to extract features and classify anomalies from surveillance footage. A Quantum-based Circuit, such as the real amplitude circuit, is utilized to improve the performance of the model. As far as my research, this is the first work to employ quantum deep learning techniques to classify anomalous events in video surveillance applications. There are 13 anomalies classified from the UCF-crime dataset. Based on experimental results, the proposed model is capable of efficiently classifying data concerning confusion matrix, Receiver Operating Characteristic (ROC), accuracy, Area Under Curve (AUC), precision, recall as well as F1-score. The proposed QC-CNN has attained the best accuracy of 95.65 percent which is 5.37% greater when compared to other existing models. To measure the efficiency of the proposed work, QC-CNN is also evaluated with classical and quantum models.

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APA Style
Arafath, M.Y., Kumar, A.N. (2023). Quantum computing based neural networks for anomaly classification in real-time surveillance videos. Computer Systems Science and Engineering, 46(2), 2489-2508. https://doi.org/10.32604/csse.2023.035732
Vancouver Style
Arafath MY, Kumar AN. Quantum computing based neural networks for anomaly classification in real-time surveillance videos. Comput Syst Sci Eng. 2023;46(2):2489-2508 https://doi.org/10.32604/csse.2023.035732
IEEE Style
M.Y. Arafath and A.N. Kumar, "Quantum Computing Based Neural Networks for Anomaly Classification in Real-Time Surveillance Videos," Comput. Syst. Sci. Eng., vol. 46, no. 2, pp. 2489-2508. 2023. https://doi.org/10.32604/csse.2023.035732



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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