CCTV Image Sequence Generation and Modeling Method for Video Anomaly Detection Using Generative Adversarial Network

Shin, Wonsup; Cho, Sung Bae

doi:10.1007/978-3-030-03493-1_48

Cited by 10 publications

(9 citation statements)

References 18 publications

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“…To address this problem, Schlegl et al proposed an additional step after training the GAN on normal data. For an image x, they proposed to find a point z in the latent space that corresponds to an image G(z), which is the most similar to the image Type of GAN List of references DCGAN [18], [22], [25], [30]- [33], [35], [40], [44], [49], [53], [55], [60], [66], [69], [77], [78], [81], [84], [86], [88], [92], [95]- [97], [99], [103]- [105], [108], [109] Standard GAN [16], [21], [24], [36], [38], [43], [45], [46], [51], [52], [54], [58], [59], [61], [70], [75], [79], [83], [87], [93], …”

Section: ) Representation Learning With Generative Adversarial Networkmentioning

confidence: 99%

“…Autonomy is defined as self-governance or freedom from external influences [156]. An autonomous system is referred to as a system that can perceive the environment, make decisions based on [18], [26], [29], [30], [42], [59], [78], [80], [85], [97], [98], [101], [104], [105], [123], [132]- [135], [137], [138], [141], [143] Surveillance [20]- [22], [24], [31], [37], [39], [41], [43], [54], [64], [65], [74], [75], [81], [90], [91], [109], [122] Intrusion Detection [19], [46], [76], [82], [83], [87], [93], [106], [110],…”

Section: ) Data Augmentation With Generative Adversarial Networkmentioning

confidence: 99%

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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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Section: ) Representation Learning With Generative Adversarial Networkmentioning

confidence: 99%

Section: ) Data Augmentation With Generative Adversarial Networkmentioning

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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“…Precision, F1-score, accuracy, recall, sensitivity, equal error rate (EER), specificity, and receiver operating curve (ROC) are also frequently used as metrics in this area. San Francisco cabspotting: [115] SBHAR: [46] SD-OCT: [17] Sentence polarity: [68] ShanghAaiTech: [41,75] SIXray: [91] Spectralis OCT: [26] SWaT system: [93] SVHN: [50,62] TalkingData AdTracking: [113,67] Tennessee eastman: [16,28] Texas coast: [27] Thyroid: [132] UBA: [96] UCI: [38,126] UCSD: [21,22,37,39,41,54,64,65,122,74,75,79,90,107,109] Udacity: [56,61] UMN: [39,43,64,65,74,90,107] UNSW-NB15: [110] VIRAT: [81] WADI test-bed: [93] WOA13 month...…”

Section: Rq4: Which Type Of Data Instance and Datasets Are Most Commo...mentioning

confidence: 99%

“…Structural similarity indices metrics (SSIM): [116,55,56,63,65,98,99] Peak signal to noise ratio (PSNR): [21,22,24,41,44,137,85] Visual inspection: [17,40,60,69,139,135] Fréchet inception distance (FID): [124,82] Signal to noise ratio (SNR): [53] L2-norm distance: [109] Fully convolutional network (FCN)-score: [65] Earth mover's distance: [21] Cosine similarity: [109] [116,55,56,63,65,98,99] Peak signal to noise ratio (PSNR): [21,22,24,41,44,137,85] Visual inspection: [17,40,60,69,139,135] Fréchet inception distance (FID): [124,…”

Section: Type Of Performance Metrics Used Image Tabular Video Time Se...mentioning

confidence: 99%

“…Support vector machines [110,113,114,118,119,136,125,126,127,129, 109] Neural network-based methods [110,113,117,121,124,125,106,139,129,91,140,141,143,134,109] Nearest neighbors [114,119,136,121,109] Naive Bayes [110,119,121,129] Ensemble methods [110,113,114,119,136,121,129,131, 109] Linear model [113,114,119,121,121,126] Decision trees [110,114...…”

Section: Techniques Referencesmentioning

confidence: 99%

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

Sabuhi¹,

Zhou²,

Bezemer³

et al. 2021

Preprint

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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 tumours. Over time, many anomaly detection techniques have been introduced. However, in general, they all suffer from the same problem: a 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 a great deal of attention in anomaly detection research, due to their unique ability to generate new data. In this paper, we present a systematic literature review of the applications of GANs in anomaly detection, covering 128 papers on the subject. The goal of this review paper is to analyze and summarize: (1) which anomaly detection techniques can benefit from certain types of GANs, and how, (2) in which application domains GAN-assisted anomaly detection techniques have been applied, and (3) which datasets and performance metrics have been used to evaluate these techniques. 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.

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LIREM: A Generic Framework for Effective Online Video Novelty Detection

Zhou

Wang

2022

Lecture Notes in Computer Science

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CCTV Image Sequence Generation and Modeling Method for Video Anomaly Detection Using Generative Adversarial Network

Cited by 10 publications

References 18 publications

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

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

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

LIREM: A Generic Framework for Effective Online Video Novelty Detection

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