A Deep Learning Approach to Video Anomaly Detection using Convolutional Autoencoders

2023/11/07 by Gopikrishna Pavuluri, Pavuluri, Gopikrishna, Gayathri Annem +1
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Artificial Immune Systems Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Network Security and Intrusion Detection

paper · pdf · doi:10.48550/arxiv.2311.04351

openalex publication_date 2023/11/07 · openalex created_date 2023/11/10 · openalex updated_date 2026/07/28

Abstract

In this research we propose a deep learning approach for detecting anomalies in videos using convolutional autoencoder and decoder neural networks on the UCSD dataset.Our method utilizes a convolutional autoencoder to learn the spatiotemporal patterns of normal videos and then compares each frame of a test video to this learned representation. We evaluated our approach on the UCSD dataset and achieved an overall accuracy of 99.35% on the Ped1 dataset and 99.77% on the Ped2 dataset, demonstrating the effectiveness of our method for detecting anomalies in surveillance videos. The results show that our method outperforms other state-of-the-art methods, and it can be used in real-world applications for video anomaly detection.

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