A Deep Learning Approach to Video Anomaly Detection using Convolutional Autoencoders
This addresses anomaly detection in surveillance videos, but it is incremental as it applies an existing deep learning method to a known dataset.
The researchers tackled video anomaly detection by using a convolutional autoencoder to learn normal spatiotemporal patterns, achieving 99.35% accuracy on the Ped1 dataset and 99.77% on the Ped2 dataset.
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.