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VENUS: A System for Novelty Detection in Video Streams with Learning

AAAI Conferences

Novelty detection in video is a rapidly developing application domain within computer vision. The motivation behind this paper is a learning based framework for detecting novelty within video. Since, humans have a general understanding about their environment and possess a sense of distinction between what is normal and abnormal about the environment based on our prior experience; any aspect of the scene that does not fit into this definition of normalcy tends to be labeled as a novel event. In this paper, we propose a computational learning based framework for novelty detection and provide the experimental evidence to describe the results obtained by this framework. To begin with the framework extracts low-level features from scenes, based on the focus of attention theory and then combines unsupervised learning techniques such as clustering with habituation theory to emulate the cognitive aspect of learning.


q-Space Novelty Detection with Variational Autoencoders

arXiv.org Machine Learning

In machine learning, novelty detection is the task of identifying novel unseen data. During training, only samples from the normal class are available. Test samples are classified as normal or abnormal by assignment of a novelty score. Here we propose novelty detection methods based on training variational autoencoders (VAEs) on normal data. Since abnormal samples are not used during training, we define novelty metrics based on the (partially complementary) assumptions that the VAE is less capable of reconstructing abnormal samples well; that abnormal samples more strongly violate the VAE regularizer; and that abnormal samples differ from normal samples not only in input-feature space, but also in the VAE latent space and VAE output. These approaches, combined with various possibilities of using (e.g.~sampling) the probabilistic VAE to obtain scalar novelty scores, yield a large family of methods. We apply these methods to magnetic resonance imaging, namely to the detection of diffusion-space (\mbox{q-space}) abnormalities in diffusion MRI scans of multiple sclerosis patients, i.e.~to detect multiple sclerosis lesions without using any lesion labels for training. Many of our methods outperform previously proposed q-space novelty detection methods.


Enhanced Telemetry Monitoring with Novelty Detection

AI Magazine

This approach consists of defining an upper and lower threshold so that when a measurement goes above the upper limit or below the lower one, an alarm is triggered. We discuss the limitations of the Out-Of-Limits approach and propose a new monitoring paradigm based on novelty detection. The proposed monitoring approach can detect novel behaviors, which are often signatures of anomalies, very early -- allowing engineers in some cases to react before the anomaly develops. A prototype implementing this monitoring approach has been implemented and applied to several ESA missions.


Enhanced Telemetry Monitoring with Novelty Detection

AI Magazine

This approach consists of defining an upper and lower threshold so that when a measurement goes above the upper limit or below the lower one, an alarm is triggered. We discuss the limitations of the out-of-limits approach and propose a new monitoring paradigm based on novelty detection. The proposed monitoring approach can detect novel behaviors, which are often signatures of anomalies, very early -- allowing engineers in some cases to react before the anomaly develops. A prototype implementing this monitoring approach has been implemented and applied to several ESA missions. The operational assessment from the XMM-Newton operations team is presented.


Enhanced Telemetry Monitoring with Novelty Detection

AI Magazine

Typically, automatic telemetry monitoring in space operations is performed by Out-of-Limits (OOL) alarms. This approach consists of defining an upper and lower threshold so that when a measurement goes above the upper limit or below the lower one, an alarm is triggered. We discuss the limitations of the Out-Of-Limits approach and propose a new monitoring paradigm based on novelty detection. The proposed monitoring approach can detect novel behaviors, which are often signatures of anomalies, very early — allowing engineers in some cases to react before the anomaly develops. A prototype implementing this monitoring approach has been implemented and applied to several ESA missions. The operational assessment from the XMM-Newton operations team is presented.