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.
@article{arxiv.2311.04351,
title = {A Deep Learning Approach to Video Anomaly Detection using Convolutional Autoencoders},
author = {Gopikrishna Pavuluri and Gayathri Annem},
journal= {arXiv preprint arXiv:2311.04351},
year = {2023}
}