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

Latent Space Autoregression for Novelty Detection

Novelty detection is commonly referred to as the discrimination of observations that do not conform to a learned model of regularity. Despite its importance in different application settings, designing a novelty detector is utterly complex due to the unpredictable nature of novelties and its inaccessibility during the training procedure, factors which expose the unsupervised nature of the problem. In our proposal, we design a general framework where we equip a deep autoencoder with a parametric density estimator that learns the probability distribution underlying its latent representations through an autoregressive procedure. We show that a maximum likelihood objective, optimized in conjunction with the reconstruction of normal samples, effectively acts as a regularizer for the task at hand, by minimizing the differential entropy of the distribution spanned by latent vectors. In addition to providing a very general formulation, extensive experiments of our model on publicly available datasets deliver on-par or superior performances if compared to state-of-the-art methods in one-class and video anomaly detection settings. Differently from prior works, our proposal does not make any assumption about the nature of the novelties, making our work readily applicable to diverse contexts

ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detection

Knowledge Distillation-based Anomaly Detection (KDAD) methods rely on the teacher-student paradigm to detect and segment anomalous regions by contrasting the unique features extracted by both networks. However, existing KDAD methods suffer from two main limitations: 1) the student network can effortlessly replicate the teacher network's representations, and 2) the features of the teacher network serve solely as a ``reference standard" and are not fully leveraged. Toward this end, we depart from the established paradigm and instead propose an innovative approach called Asymmetric Distillation Post-Segmentation (ADPS). Our ADPS employs an asymmetric distillation paradigm that takes distinct forms of the same image as the input of the teacher-student networks, driving the student network to learn discriminating representations for anomalous regions. Meanwhile, a customized Weight Mask Block (WMB) is proposed to generate a coarse anomaly localization mask that transfers the distilled knowledge acquired from the asymmetric paradigm to the teacher network. Equipped with WMB, the proposed Post-Segmentation Module (PSM) is able to effectively detect and segment abnormal regions with fine structures and clear boundaries. Experimental results demonstrate that the proposed ADPS outperforms the state-of-the-art methods in detecting and segmenting anomalies. Surprisingly, ADPS significantly improves Average Precision (AP) metric by 9% and 20% on the MVTec AD and KolektorSDD2 datasets, respectively.Comment: 11pages,9 figure

多変量時系列データの変分オートエンコーダによるロバストな教示なし異常検知

九州工業大学博士学位論文 学位記番号：情工博甲第370号 学位授与年月日：令和4年9月26日1: Introduction|2: Background & Theory|3: Methodology|4: Experiments and Discussion|5: Conclusions九州工業大学令和4年

Visual Anomaly Detection on Circular Plastic Parts Using Generative Adversarial Networks

openIn the recent years, automated quality control systems have been estabilished as the main method for anomaly detection, term which refers to the process of identifying and flagging any abnormality in the condition of the given components. Given their efficiency, many new methods were developed, mainly exploiting Computer Vision algorithms, but they have their limitations. In a similar way, many studies were applied on Neural Networks and Machine Learning algorithms, with the development of Convolutional Neural Networks, Transformers and Generative Adversarial Networks (GANs). The objective of this thesis is to develop an automated quality control system exploiting the generative and adversarial qualities of the current state-of-the-art methods based on Neural Networks. The main tool used for this task is the capability of the GANs to learn how a flawless input should look, so that the pipeline can identify inputs with anomalies. The developed solution was tested on a real world problem, aiming to indentify cracks and anomalies in plastic motor covers.In the recent years, automated quality control systems have been estabilished as the main method for anomaly detection, term which refers to the process of identifying and flagging any abnormality in the condition of the given components. Given their efficiency, many new methods were developed, mainly exploiting Computer Vision algorithms, but they have their limitations. In a similar way, many studies were applied on Neural Networks and Machine Learning algorithms, with the development of Convolutional Neural Networks, Transformers and Generative Adversarial Networks (GANs). The objective of this thesis is to develop an automated quality control system exploiting the generative and adversarial qualities of the current state-of-the-art methods based on Neural Networks. The main tool used for this task is the capability of the GANs to learn how a flawless input should look, so that the pipeline can identify inputs with anomalies. The developed solution was tested on a real world problem, aiming to indentify cracks and anomalies in plastic motor covers

A Survey on Explainable Anomaly Detection

In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding methods and thus leaving the explanation of outcomes to practitioners. As anomaly detection algorithms are increasingly used in safety-critical domains, providing explanations for the high-stakes decisions made in those domains has become an ethical and regulatory requirement. Therefore, this work provides a comprehensive and structured survey on state-of-the-art explainable anomaly detection techniques. We propose a taxonomy based on the main aspects that characterize each explainable anomaly detection technique, aiming to help practitioners and researchers find the explainable anomaly detection method that best suits their needs.Comment: Paper accepted by the ACM Transactions on Knowledge Discovery from Data (TKDD) for publication (preprint version

Comparative Evaluation and Implementation of State-of-the-Art Techniques for Anomaly Detection and Localization in the Continual Learning Framework

openThe capability of anomaly detection (AD) to detect defects in industrial environments using only normal samples has attracted significant attention. However, traditional AD methods have primarily concentrated on the current set of examples, leading to a significant drawback of catastrophic forgetting when faced with new tasks. Due to the constraints in flexibility and the challenges posed by real-world industrial scenarios, there is an urgent need to strengthen the adaptive capabilities of AD models. Hence, this thesis introduces a unified framework that integrates continual learning (CL) and anomaly detection (AD) to accomplish the goal of anomaly detection in the continual learning (ADCL). To evaluate the effectiveness of the framework, a comparative analysis is performed to assess the performance of the three specific feature-based methods for the AD task: Coupled-Hypersphere-Based Feature Adaptation (CFA), Student-Teacher approach, and PatchCore. Furthermore, the framework incorporates the utilization of replay techniques to facilitate continual learning (CL). A comprehensive evaluation is conducted using a range of metrics to analyze the relative performance of each technique and identify the one that exhibits superior results. To validate the effectiveness of the proposed approach, the MVTec AD dataset, consisting of real-world images with pixel-based anomalies, is utilized. This dataset serves as a reliable benchmark for Anomaly Detection in the context of Continual Learning, providing a solid foundation for further advancements in the field.The capability of anomaly detection (AD) to detect defects in industrial environments using only normal samples has attracted significant attention. However, traditional AD methods have primarily concentrated on the current set of examples, leading to a significant drawback of catastrophic forgetting when faced with new tasks. Due to the constraints in flexibility and the challenges posed by real-world industrial scenarios, there is an urgent need to strengthen the adaptive capabilities of AD models. Hence, this thesis introduces a unified framework that integrates continual learning (CL) and anomaly detection (AD) to accomplish the goal of anomaly detection in the continual learning (ADCL). To evaluate the effectiveness of the framework, a comparative analysis is performed to assess the performance of the three specific feature-based methods for the AD task: Coupled-Hypersphere-Based Feature Adaptation (CFA), Student-Teacher approach, and PatchCore. Furthermore, the framework incorporates the utilization of replay techniques to facilitate continual learning (CL). A comprehensive evaluation is conducted using a range of metrics to analyze the relative performance of each technique and identify the one that exhibits superior results. To validate the effectiveness of the proposed approach, the MVTec AD dataset, consisting of real-world images with pixel-based anomalies, is utilized. This dataset serves as a reliable benchmark for Anomaly Detection in the context of Continual Learning, providing a solid foundation for further advancements in the field