A Deep Learning Approach to Video Anomaly Detection using Convolutional Autoencoders

TL;DR

This paper presents a deep learning method using convolutional autoencoders to detect anomalies in surveillance videos, achieving high accuracy on the UCSD dataset and outperforming existing techniques.

Contribution

The paper introduces a convolutional autoencoder-based approach for video anomaly detection that learns normal spatiotemporal patterns and effectively identifies anomalies.

Findings

Achieved 99.35% accuracy on UCSD Ped1 dataset

Achieved 99.77% accuracy on UCSD Ped2 dataset

Outperformed existing state-of-the-art methods

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.