A Deep Learning Approach to Video Anomaly Detection using Convolutional Autoencoders

Gopikrishna Pavuluri; Gayathri Annem

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arXiv · 2311.04351

A Deep Learning Approach to Video Anomaly Detection using Convolutional Autoencoders

Gopikrishna Pavuluri·Gayathri Annem

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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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BibTeX RIS

Gopikrishna Pavuluri, Gayathri Annem. 2023-11-07. A Deep Learning Approach to Video Anomaly Detection using Convolutional Autoencoders. https://arxiv.org/abs/2311.04351

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