1,261 research outputs found
MTGFlow: Unsupervised Multivariate Time Series Anomaly Detection via Dynamic Graph and Entity-aware Normalizing Flow
Multivariate time series anomaly detection has been extensively studied under
the semi-supervised setting, where a training dataset with all normal instances
is required. However, preparing such a dataset is very laborious since each
single data instance should be fully guaranteed to be normal. It is, therefore,
desired to explore multivariate time series anomaly detection methods based on
the dataset without any label knowledge. In this paper, we propose MTGFlow, an
unsupervised anomaly detection approach for Multivariate Time series anomaly
detection via dynamic Graph and entity-aware normalizing Flow, leaning only on
a widely accepted hypothesis that abnormal instances exhibit sparse densities
than the normal. However, the complex interdependencies among entities and the
diverse inherent characteristics of each entity pose significant challenges on
the density estimation, let alone to detect anomalies based on the estimated
possibility distribution. To tackle these problems, we propose to learn the
mutual and dynamic relations among entities via a graph structure learning
model, which helps to model accurate distribution of multivariate time series.
Moreover, taking account of distinct characteristics of the individual
entities, an entity-aware normalizing flow is developed to describe each entity
into a parameterized normal distribution, thereby producing fine-grained
density estimation. Incorporating these two strategies, MTGFlowachieves
superior anomaly detection performance. Experiments on the real-world datasets
are conducted, demonstrating that MTGFlow outperforms the state-of-the-art
(SOTA) by 5.0% and 1.6% AUROC for SWaT and WADI datasets respectively. Also,
through the anomaly scores contributed by individual entities, MTGFlow can
provide explanation information for the detection results
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos
Videos represent the primary source of information for surveillance
applications and are available in large amounts but in most cases contain
little or no annotation for supervised learning. This article reviews the
state-of-the-art deep learning based methods for video anomaly detection and
categorizes them based on the type of model and criteria of detection. We also
perform simple studies to understand the different approaches and provide the
criteria of evaluation for spatio-temporal anomaly detection.Comment: 15 pages, double colum
Dynamic Circular Network-Based Federated Dual-View Learning for Multivariate Time Series Anomaly Detection
Multivariate time-series data exhibit intricate correlations in both temporal and spatial dimensions. However, existing network architectures often overlook dependencies in the spatial dimension and struggle to strike a balance between long-term and short-term patterns when extracting features from the data. Furthermore, industries within the business community are hesitant to share their raw data, which hinders anomaly prediction accuracy and detection performance. To address these challenges, the authors propose a dynamic circular network-based federated dual-view learning approach. Experimental results from four open-source datasets demonstrate that the method outperforms existing methods in terms of accuracy, recall, and F1_score for anomaly detection
Normalizing Flows for Human Pose Anomaly Detection
Video anomaly detection is an ill-posed problem because it relies on many
parameters such as appearance, pose, camera angle, background, and more. We
distill the problem to anomaly detection of human pose, thus reducing the risk
of nuisance parameters such as appearance affecting the result. Focusing on
pose alone also has the side benefit of reducing bias against distinct minority
groups. Our model works directly on human pose graph sequences and is
exceptionally lightweight ( parameters), capable of running on any
machine able to run the pose estimation with negligible additional resources.
We leverage the highly compact pose representation in a normalizing flows
framework, which we extend to tackle the unique characteristics of
spatio-temporal pose data and show its advantages in this use case. Our
algorithm uses normalizing flows to learn a bijective mapping between the pose
data distribution and a Gaussian distribution, using spatio-temporal graph
convolution blocks. The algorithm is quite general and can handle training data
of only normal examples, as well as a supervised dataset that consists of
labeled normal and abnormal examples. We report state-of-the-art results on two
anomaly detection benchmarks - the unsupervised ShanghaiTech dataset and the
recent supervised UBnormal dataset
Spatio-Temporal Anomaly Detection for Industrial Robots through Prediction in Unsupervised Feature Space
International audienceSpatio-temporal anomaly detection by unsupervised learning have applications in a wide range of practical settings. In this paper we present a surveillance system for industrial robots using a monocular camera. We propose a new unsupervised learning method to train a deep feature extractor from unlabeled images. Without any data augmentation , the algorithm co-learns the network parameters on different pseudo-classes simultaneously to create unbiased feature representation. Combining the learned features with a prediction system, we can detect irregularities in high dimensional data feed (e.g. video of a robot performing pick and place task). The results show how the proposed approach can detect previously unseen anomalies in the robot surveillance video. Although the technique is not designed for classification, we show the use of the learned features in a more traditional classification application for CIFAR-10 dataset
- …