Video surveillance and counterterrorism: the application of suspicious activity recognition in visual surveillance systems to counterterrorism

Mould, Nick; Regens, James L.; Jensen, Carl; Edger, David N.

doi:10.1080/18335330.2014.940819

Cited by 9 publications

(4 citation statements)

References 42 publications

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“…One solution for the first challenge lies in shepherding (Long et al, 2020), a bio-inspired swarm guidance method that mimics how sheepdogs guide a swarm of sheep. The concept of shepherding for swarm guidance has been applied in many applications including agriculture (Strömbom et al, 2014), crowd control (Li et al, 2012; Mould et al, 2014) and uninhabited vehicle (UxV) navigation (Abbass & Hunjet, 2021b), and has proved viable in limited communication settings (Mohamed et al, 2021). The two remaining challenges also exist in shepherding research.…”

Section: Introductionmentioning

confidence: 99%

Swarm analytics: Designing information markers to characterise swarm systems in shepherding contexts

et al. 2022

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Contemporary swarm indicators are often used in isolation, focussed on extracting information at the individual or collective levels. Consequently, these are seldom integrated to infer a top-level operating picture of the swarm, its members and its overall collective dynamics. The primary contribution of this paper is to organise a suite of indicators about swarms into an ontologically arranged collection of information markers to characterise the swarm from the perspective of an external observer – , a recognition agent. Our contribution shows the foundations for a new area of research that we title swarm analytics, whose primary concern is with the design and organisation of collections of swarm markers to understand, detect, recognise, track and learn a particular insight about a swarm system. We present our designed framework of information markers that offer a new avenue for swarm research, especially for heterogeneous and cognitive swarms that may require more advanced capabilities to detect agencies and categorise agent influences and responses.

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Section: Introductionmentioning

confidence: 99%

Swarm analytics: Designing information markers to characterise swarm systems in shepherding contexts

et al. 2022

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“…Anomaly detection is an important field in computer vision. The detection of abnormal images plays an increasingly important role due to the growing demand in various applications, such as video surveillance, risk management and damage detection [8,14]. Current state of the art methods in this area are based on deep learning methods such as Deep-anomaly [17] and ADCNN [10].…”

Section: Introductionmentioning

confidence: 99%

A Transformer-Based GAN for Anomaly Detection

Yang¹,

Lan²,

Huang³

et al. 2022

Lecture Notes in Computer Science

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Anomaly detection is the task of detecting outliers from normal data. Numerous methods have been proposed to address this problem, including recent methods based on generative adversarial network (GAN). However, these methods are limited in capturing the long-range information in data due to the limited receptive field obtained by the convolution operation. The long-range information is crucial for producing distinctive representation for normal data belonging to different classes, while the local information is important for distinguishing normal data from abnormal data, if they belong to the same class. In this paper, we propose a novel Transformer-based architecture for anomaly detection which has advantages in extracting features with global information representing different classes as well as the local details useful for capturing anomalies. In our design, we introduce self-attention mechanism into the generator of GAN to extract global semantic information, and also modify the skip-connection to capture local details in multi-scale from input data. The experiments on CIFAR10 and STL10 show that our method provides better performance on representing different classes as compared with the state-of-the-art CNN-based GAN methods. Experiments performed on MVTecAD and LBOT datasets show that the proposed method offers state-of-the-art results, outperforming the baseline method SAGAN by over 3% in terms of the AUC metric.

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“…Similarly, with an increasing need to ensure public safety in crowded areas, the development of real-time video surveillance systems becomes unavoidable. It is critical to seamlessly monitor the crowd to immediately detect anomalous (or abnormal) movements to help prevent theft [4], vandalism [5], and terrorist attacks [6].…”

Section: Introductionmentioning

confidence: 99%

Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

Sabuhi¹,

Zhou

Bezemer

et al. 2021

IEEE Access

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Anomaly detection has become an indispensable tool for modern society, applied in a wide range of applications, from detecting fraudulent transactions to malignant brain tumors. Over time, many anomaly detection techniques have been introduced. However, in general, they all suffer from the same problem: lack of data that represents anomalous behaviour. As anomalous behaviour is usually costly (or dangerous) for a system, it is difficult to gather enough data that represents such behaviour. This, in turn, makes it difficult to develop and evaluate anomaly detection techniques. Recently, generative adversarial networks (GANs) have attracted much attention in anomaly detection research, due to their unique ability to generate new data. In this paper, we present a systematic review of the literature in this area, covering 128 papers. The goal of this review paper is to analyze the relation between anomaly detection techniques and types of GANs, to identify the most common application domains for GAN-assisted and GAN-based anomaly detection, and to assemble information on datasets and performance metrics used to assess them. Our study helps researchers and practitioners to find the most suitable GAN-assisted anomaly detection technique for their application. In addition, we present a research roadmap for future studies in this area. In summary, GANs are used in anomaly detection to address the problem of insufficient amount of data for the anomalous behaviour, either through data augmentation or representation learning. The most commonly used GAN architectures are DCGANs, standard GANs, and cGANs. The primary application domains include medicine, surveillance and intrusion detection.

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Video surveillance and counterterrorism: the application of suspicious activity recognition in visual surveillance systems to counterterrorism

Cited by 9 publications

References 42 publications

Swarm analytics: Designing information markers to characterise swarm systems in shepherding contexts

Swarm analytics: Designing information markers to characterise swarm systems in shepherding contexts

A Transformer-Based GAN for Anomaly Detection

Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

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