A Deep Learning Approach to Video Anomaly Detection using Convolutional Autoencoders

• URL: http://arxiv.org/abs/2311.04351v1
• Date: Tue, 7 Nov 2023 21:23:32 GMT
• Title: A Deep Learning Approach to Video Anomaly Detection using Convolutional Autoencoders
• Authors: Gopikrishna Pavuluri, Gayathri Annem
• Abstract summary: Our method utilizes a convolutional autoencoder to learn the patterns of normal videos and then compares each frame of a test video to this learned representation. We evaluated our approach and achieved an overall accuracy of 99.35% on the Ped1 dataset and 97% on the Ped2 dataset. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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