Augmenting Anomaly Detection Datasets with Reactive Synthetic Elements

Abstract

Automatic anomaly detection for surveillance purposes has become an integral part of accident prevention and early warning systems. The lack of sufficient real datasets for training and testing such detectors has pushed a lot of research into synthetic data generation. A hybrid approach by combining real images with synthetic elements has been proven to produce the best training results.We aim to extend this hybrid approach by combining the backgrounds and real people captured in datasets with synthetic elements which dynamically react to real pedestrians and create more coherent video sequences. Our pipeline is the first to directly augment synthetic objects like handbags and suitcases to real pedestrians and provides dynamic occlusion between real and synthetic elements in the images. The pipeline can be easily used to produce a continuous stream of randomized augmented normal and abnormal data for training and testing. As a basis for our augmented images, we use one of the most widely used classical datasets for anomaly detection - the UCSD dataset. We show that the synthetic data produced by our proposed pipeline can be used to make the dataset harder for state-of-the-art models, by introducing more varied and challenging anomalies. We also demonstrate that the additional synthetic normal data can boost the performance of some models. Our solution can be easily extended with additional 3D models, animations, and anomaly scenarios.