  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.
