A Hybrid Deep Learning Model with Generative Adversarial Network for Abnormality Detection from Surveillance Videos

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 7

Year of Publication : 2025

Authors : Ganta Raju, G. S. Naveen Kumar

10.14445/23488549/IJECE-V12I7P122

How to Cite?

Ganta Raju, G. S. Naveen Kumar, "A Hybrid Deep Learning Model with Generative Adversarial Network for Abnormality Detection from Surveillance Videos," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 7, pp. 280-294, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I7P122

Abstract:

Video anomaly detection is a real-world problem that artificial intelligence (AI) and computer vision applications can solve. In today's world, with our deteriorating environment, it is urgent to assess surveillance videos and process them in real-time for abnormalities to ensure the safety and security of citizens. Several deep-learning approaches have been developed to detect anomalies in videos. However, these traditional models require improvements in hybridization and utilize a Generative Adversarial Network (GAN) architecture to achieve enhanced performance. This paper presents a novel deep-learning framework that efficiently addresses this problem by searching surveillance footage for irregularities. A deep learning technique called GANDL-VAD is suggested for Video Abnormal Detection (VAD). It uses a GAN architecture and offers a hybrid DL model that considers both extracted and synthesized data, thereby improving the efficiency of detection and classification. The suggested hybrid deep learning model surpassed its modern rivals with an accuracy rate of 98.78%, according to experimental results on the UCF-Crime benchmark dataset. It can be used directly in current computer vision applications for video analytics and is capable of detecting anomaly events in surveillance videos.

Keywords:

Video abnormality detection, Artificial Intelligence, Hybrid Deep Learning, Generative Adversarial Network, Computer vision.

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