Self-Supervised Learning for Industrial Image Anomaly Detection by Simulating Anomalous Samples

Semi-supervised Learning for Anomalous Trajectory Detection

A novel learning framework is proposed for anomalous behaviour detection in a video surveillance scenario, so that a classifier which distinguishes between normal and anomalous behaviour patterns can be incrementally trained with the assistance of a human operator. We consider the behaviour of pedestrians in terms of motion trajectories, and parametrise these trajectories using the control poin...

Incremental semi-supervised learning for anomalous trajectory detection

The acquisition of a scene-specific normal behaviour model underlies many existing approaches to the problem of automated video surveillance. Since it is unrealistic to acquire a comprehensive set of labelled behaviours for every surveyed scenario, modelling normal behaviour typically corresponds to modelling the distribution of a large collection of unlabelled examples. In general, however, it...

Toward Supervised Anomaly Detection

Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions. However, the predictive performance of purely unsupervised anomaly detection often fails to match the required detection rates in many tasks and there exists a need for labeled data to guide the model generation. Our first contribution shows that...

ADAPTIVE GRAPH-BASED ALGORITHMS FOR CONDITIONAL ANOMALY DETECTION AND SEMI-SUPERVISED LEARNING by

Anomaly Detection for Industrial Big Data

As the Industrial Internet of Things (IIoTa) grows, systems are increasingly being monitored by arrays of sensors returning time-series data at ever-increasing ‘volume, velocity and variety’b (i.e. Industrial Big Datac). An obvious use for these data is real-time systems condition monitoring and prognostic time to failure analysis (remaining useful life, RUL). (e.g. See white papers by Senseye....