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Recent works on Video Anomaly Detection (VAD) have made advancements in the unsupervised setting, known as Unsupervised VAD (UVAD), which brings it closer to practical applications. Unlike the classic VAD task that requires a clean training set with only normal events, UVAD aims to identify abnormal frames without any labeled normal/abnormal training data. Many existing UVAD methods employ handcrafted surrogate tasks, such as frame reconstruction, to address this challenge. However, we argue that these surrogate tasks are sub-optimal solutions, inconsistent with the essence of anomaly detection. In this paper, we propose a novel approach for UVAD that directly detects anomalies based on similarities between events in videos. Our method generates representations for events while simultaneously capturing prototypical normality patterns, and detects anomalies based on whether an event's representation matches the captured patterns. The proposed model comprises a memory module to capture normality patterns, and a representation learning network to obtain representations matching the memory module for normal events. A pseudo-label generation module as well as an anomalous event generation module for negative learning are further designed to assist the model to work under the strictly unsupervised setting. Experimental results demonstrate that the proposed method outperforms existing UVAD methods and achieves competitive performance compared with classic VAD methods. [ABSTRACT FROM AUTHOR]

Video anomaly detection aims to identify a set of abnormal events in videos. Deep reconstruction and prediction-based models have been employed to detect anomalies. Deep reconstruction models sometimes recreate the abnormal events along with the normal ones. However, the prediction-based approaches have demonstrated encouraging results. This paper presents a video vision transformer (ViViT) based generative adversarial network (GAN), TransGANomaly, a novel approach for detecting anomalies. The proposed framework is a video frame predictor and trained only on normal video data adversarially. The generator of the GAN is a ViViT network that receives 3D input tokens from the video snippets. The generator aims to predict the future frame based on past sequences. After that, the predicted and original frames are given to the model's discriminator for binary classification. Extensive experiments have been performed on UCSD Pedestrian, CUHK Avenue, and ShanghiaTech datasets to validate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

In recent years, mechanical sensor data anomaly detection has gained much attention in the machine learning and mechanical fault warning fields. Limited by the fact that there is far less anomalous data available for analysis than normal data, many machine learning methods fail to perform excellent detection results. In this paper, we propose a novel Simultaneous Generation and Anomaly Detection with Generative Adversarial Networks (SGAD-GAN) framework to tackle the challenge of anomaly detection under imbalanced datasets. In our framework, the generators accomplish transfer between sensor signals while synthesizing realistic data. In addition to the regular discriminators for identifying the authenticity of samples, we design a classification discriminator to facilitate data synthesis in the direction benefitting anomaly detection, which is trained to act as an anomaly detector in an identical way as other discriminators. We conduct extensive data synthesis and anomaly detection experiments on Hydraulic System Sensor (HSS) data from the Jiaolong deep-sea manned submersible, and show the generalization ability of our approach to different application domains on the public dataset KDDCUP99. The experimental results demonstrate that our proposed algorithm outperforms several state-of-the-art data augmentation and anomaly detection methods. [ABSTRACT FROM AUTHOR]

The occurrence of manufacturing defects in wind turbine blade (WTB) production can result in significant increases in operation and maintenance costs of WTBs, reduce capacity factors of wind farms, and occasionally lead to severe and disastrous consequences. Therefore, inspection during the manufacturing process is crucial to ensure consistent fabrication of composite materials. Non-contact sensing techniques, such as Frequency Modulated Continuous Wave (FMCW) radar, are becoming increasingly popular as they offer a full view – cross sectional analysis – of these complex structures during assembly and curing. In this paper, we enhance the quality assurance of WTB manufacturing utilising FMCW radar as a non-destructive sensing modality. Additionally, a novel anomaly detection pipeline is developed that offers the following advantages: (1) We use the analytic representation of the Intermediate Frequency signal of the FMCW radar as a feature to disentangle material-specific and round-trip delay information from the received wave. (2) We propose a novel anomaly detection methodology called focus Support Vector Data Description (focus-SVDD). This methodology involves defining the limit boundaries of the dataset after removing healthy data features, thereby focusing on the attributes of anomalies. (3) The proposed method employs a complex-valued autoencoder to remove healthy features and we introduces a new activation function called Exponential Amplitude Decay (EAD). EAD takes advantage of the Rayleigh distribution, which characterises an instantaneous amplitude signal. The effectiveness of the proposed method is demonstrated through its application to collected data, where it shows superior performance compared to other state-of-the-art unsupervised anomaly detection methods. This method is expected to make a significant contribution not only to structural health monitoring but also to the field of deep complex-valued data processing and SVDD application. The code and dataset will be made publicly available. The code and dataset are available here 1 1 https://github.com/FrusqueGaetan/Focus_SVDD_EAD. [ABSTRACT FROM AUTHOR]