Augmenting Anomaly Detection Datasets with Reactive Synthetic Elements

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

Rough or Noisy? Metrics for Noise Estimation in SfM Reconstructions

Structure from Motion (SfM) can produce highly detailed 3D reconstructions, but distinguishing real surface roughness from reconstruction noise and geometric inaccuracies has always been a difficult problem to solve. Existing SfM commercial solutions achieve noise removal by a combination of aggressive global smoothing and the reconstructed texture for smaller details, which is a subpar solution when the results are used for surface inspection. Other noise estimation and removal algorithms do not take advantage of all the additional data connected with SfM. We propose a number of geometrical and statistical metrics for noise assessment, based on both the reconstructed object and the capturing camera setup. We test the correlation of each of the metrics to the presence of noise on reconstructed surfaces and demonstrate that classical supervised learning methods, trained with these metrics can be used to distinguish between noise and roughness with an accuracy above 85%, with an additional 5–6% performance coming from the capturing setup metrics. Our proposed solution can easily be integrated into existing SfM workflows as it does not require more image data or additional sensors. Finally, as part of the testing we create an image dataset for SfM from a number of objects with varying shapes and sizes, which are available online together with ground truth annotations