Detection of Video Anomalies Using Convolutional Autoencoders and One-Class Support Vector Machines

With the growth of image data being generated by surveillance cameras, automated video analysis has become necessary in order to detect unusual events. Recently, Deep Learning methods have achieved the state of the art results in many tasks related to computer vision. Among Deep Learning methods, the Autoencoder is commonly used for anomaly detection tasks. This work presents a method to classify frames of four different well known video datasets as normal or anomalous by using reconstruction errors as features for a classifier. To perform this task, Convolutional Autoencoders and One-Class SVMs were employed. Results suggest that the method is capable of detecting anomalies across the four different benchmark datasets. We also present a comparison with the state of the art approaches and data visualization.