Rare Events Detection and Localization In Crowded Scenes Based On Flow Signature

We introduce in this paper a novel method for rare events detection based on the optical flow signature. It aims to automatically highlight regions in videos where rare events are occurring. This kind of method can be used as an important step for many applications such as Closed-Circuit Television (CCTV) monitoring systems in order to reduce the cognitive effort of the operators by focusing their attention on the interesting regions. The proposed method exploits the properties of the Discrete Cosine Transform (DCT) applied to the magnitude and orientation maps of the optical flow. The output of the algorithm is a map where each pixel has a saliency score that indicates the presence of irregular motion regard to the scene. Based on the one class Support Vectors Machine (SVM) algorithm, a model of the frequent events is created and the rare events detection can be performed by using this model. The DCT is faster, easy to compute and gives interesting information to detect spatial irregular patterns in images [1]. Our method does not rely on any prior information of the scene and uses the saliency score as a feature descriptor. We demonstrate the potential of the proposed method on the publicly available videos dataset UCSD and show that it is competitive and outperforms some the state-of-the-art methods

Anomaly Detection using Variational Autoencoder with Spectrum Analysis for Time Series Data

Uncertainty is an ever present challenge in life. To meet this challenge in data analysis, we propose a method for detecting anomalies in data. This method, based in part on Variational Autoencoder, identifies spiking raw data by means of spectrum analysis. Time series data are examined in the frequency domain to enhance the detection of anomalies. In this paper, we have used the standard data sets to validate the proposed method. Experimental results show that the comparison of the frequency domain with the original data for anomaly detection can improve validity and accuracy on all criteria. Therefore, analysis of time series data by combining Variational Autoencoder and frequency domain spectrum methods can effectively detect anomalies. Contribution- We have proposed an anomaly detection method based on the time series data analysis by combining Variational Autoencoder and Spectrum analysis, and have benchmarked the method with reference to recent related research.10th International Conference on Informatics, Electronics, and Vision (ICIEV20), 26-29 August, 2020, Kitakyushu, Japa

Improvement in detection of presence in forbidden locations in video anomaly using optical flow map

Anomaly detection has been in researchers’ scope of study for a long time. The wide variety of anomaly detection use cases ranges from quality control in production lines to providing security in public places. One of the most attractive topics in anomaly detection is in video surveillance systems. In this paper, we propose a method that works based on frame prediction and optical flow to improve anomaly detection in videos. The use of optical flows in normal frames helps the system to better detect the entrance of people or objects to forbidden areas by its information about the amount of movement in different regions of the frames. Based on the optical flow of normal videos and that of current video, the threshold for anomaly decision is adaptively adjusted. This could ultimately lead to a better overall performance of the anomaly detection system compared to the recent similar works. The presented method is general and can be simply incorporated to other video anomaly detection systems to improve the detection accuracy

Deep Convolutional Variational Autoencoder as a 2D-Visualization Tool for Partial Discharge Source Classification in Hydrogenerators

International audienceHydrogenerators are strategic assets for power utilities. Their reliability and availability can lead to significant benefits. For decades, monitoring and diagnosis of hydrogenerators have been at the core of maintenance strategies. A significant part of generator diagnosis relies on Partial Discharge (PD) measurements, because the main cause of hydrogenerator breakdown comes from failure of its high voltage stator, which is a major component of hydrogenerators. A study of all stator failure mechanisms reveals that more than 85 % of them involve the presence of PD activity. PD signal can be detected from the lead of the hydrogenerator while it is running, thus allowing for on-line diagnosis. Hydro-Québec has been collecting more than 33 000 unlabeled PD measurement files over the last decades. Up to now, this diagnostic technique has been quantified based on global PD amplitudes and integrated PD energy irrespective of the source of the PD signal. Several PD sources exist and they all have different relative risk, but in order to recognize the nature of the PD, or its source, the judgement of experts is required. In this paper, we propose a new method based on visual data analysis to build a PD source classifier with a minimum of labeled data. A convolutional variational autoencoder has been used to help experts to visually select the best training data set in order to improve the performances of the PD source classifier

Auto-Encoder based Deep Representation Model for Image Anomaly Detection

Image anomaly detection is to distinguish a small portion of images that are different from the user-defined normal ones. In this work, we focus on auto-encoders based anomaly detection models, which assess the probability of anomaly by measuring reconstruction errors. One of the critical steps in image anomaly detection is to extract robust and distinguishable representations that could separate abnormal patterns from normal ones. However, current auto-encoder based methods fail to extract such distinguishable representations because their optimization objectives are not tailored for this specific task. Besides, the architectures of those models are unable to capture features that are robust to irrelevant distortions but sensitive to abnormal patterns. In this work, two auto-encoder based models are proposed to address the aforementioned issues in optimization objectives and model architectures, respectively. The first model learns to extract distinct representations for abnormal patterns by imposing sparse regularizations on the latent space during the optimization process. This sparse regularization makes the extracted abnormal features unable to be represented as sparse as the normal ones. The second model detects abnormal patterns using Asymmetric Convolution Blocks, which strengthens the crisscross part of the convolutional kernel, making the extracted features less sensitive to geometric transformations. The experimental results demonstrate the superiority of both proposed models over other auto-encoder based anomaly detection models on popular datasets. The proposed methods could also be easily incorporated into most anomaly detection methods in a plug-and-play manner

An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos

Video anomaly recognition in smart cities is an important computer vision task that plays a vital role in smart surveillance and public safety but is challenging due to its diverse, complex, and infrequent occurrence in real-time surveillance environments. Various deep learning models use significant amounts of training data without generalization abilities and with huge time complexity. To overcome these problems, in the current work, we present an efficient light-weight convolutional neural network (CNN)-based anomaly recognition framework that is functional in a surveillance environment with reduced time complexity. We extract spatial CNN features from a series of video frames and feed them to the proposed residual attention-based long short-term memory (LSTM) network, which can precisely recognize anomalous activity in surveillance videos. The representative CNN features with the residual blocks concept in LSTM for sequence learning prove to be effective for anomaly detection and recognition, validating our model’s effective usage in smart cities video surveillance. Extensive experiments on the real-world benchmark UCF-Crime dataset validate the effectiveness of the proposed model within complex surveillance environments and demonstrate that our proposed model outperforms state-of-the-art models with a 1.77%, 0.76%, and 8.62% increase in accuracy on the UCF-Crime, UMN and Avenue datasets, respectively

Unsupervised Intrusion Detection with Cross-Domain Artificial Intelligence Methods

Cybercrime is a major concern for corporations, business owners, governments and citizens, and it continues to grow in spite of increasing investments in security and fraud prevention. The main challenges in this research field are: being able to detect unknown attacks, and reducing the false positive ratio. The aim of this research work was to target both problems by leveraging four artificial intelligence techniques. The first technique is a novel unsupervised learning method based on skip-gram modeling. It was designed, developed and tested against a public dataset with popular intrusion patterns. A high accuracy and a low false positive rate were achieved without prior knowledge of attack patterns. The second technique is a novel unsupervised learning method based on topic modeling. It was applied to three related domains (network attacks, payments fraud, IoT malware traffic). A high accuracy was achieved in the three scenarios, even though the malicious activity significantly differs from one domain to the other. The third technique is a novel unsupervised learning method based on deep autoencoders, with feature selection performed by a supervised method, random forest. Obtained results showed that this technique can outperform other similar techniques. The fourth technique is based on an MLP neural network, and is applied to alert reduction in fraud prevention. This method automates manual reviews previously done by human experts, without significantly impacting accuracy

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

Design and Implementation of Anomaly Detections for User Authentication Framework

Anomaly detection is quickly becoming a very significant tool for a variety of applications such as intrusion detection, fraud detection, fault detection, system health monitoring, and event detection in IoT devices. An application that lacks a strong implementation for anomaly detection is user trait modeling for user authentication purposes. User trait models expose up-to-date representation of the user so that changes in their interests, their learning progress or interactions with the system are noticed and interpreted. The reason behind the lack of adoption in user trait modeling arises from the need of a continuous flow of high-volume data, that is not available in most cases, to achieve high-accuracy detection. This research provides new insight into anomaly detection techniques through Big Data utilization. Three classification approaches are presented for anomaly detection techniques that are aligned with Big Data characteristics: volume, variety and velocity. The classification is supported by applications of machine learning techniques, such as K-means, Hidden Markov Model, Gaussian Distribution and Auto-encoder neural network, with an aim to recommend best techniques to model user behaviour in an adaptive environment. An ingenious implementation of machine learning techniques has been presented that automatically and accurately builds a unique pattern of the users’ behaviour. With Big Data characteristics, anomaly detection techniques have become more suitable tools for user trait modeling. A solution model is designed and implemented based on anomaly detection outcomes utilizing user traits for an existing user authentication framework. User traits will be modeled by creating a security user profile for each individual user. This profile is structured and developed to be a seed for a strong real-time user authentication method. The implementation comprises four main steps: prediction of rare user actions, filter security potential actions, build/update user profile, and generate a real-time (i.e., just in time) set of challenging questions. Real-world scenarios have been given showing the benefits of these challenging questions in building secure knowledge-based user authentication systems