Transformer with Spatio-Temporal Representation for Video Anomaly Detection

Sun, X.; Chen, Jinyi; Shen, Xulin; Li, Hongjun

doi:10.1007/978-3-031-23028-8_22

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…On the other hand, most recently proposed methods have tried to improve the accuracy of anomaly detection systems by applying a modern model [130,131]. We need to consider the complexity of the model, since an anomaly detection system should be run as a real-time application.…”

Section: Research Gapsmentioning

confidence: 99%

Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey

Duong

Hoang

2023

Sensors

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Anomaly detection in video surveillance is a highly developed subject that is attracting increased attention from the research community. There is great demand for intelligent systems with the capacity to automatically detect anomalous events in streaming videos. Due to this, a wide variety of approaches have been proposed to build an effective model that would ensure public security. There has been a variety of surveys of anomaly detection, such as of network anomaly detection, financial fraud detection, human behavioral analysis, and many more. Deep learning has been successfully applied to many aspects of computer vision. In particular, the strong growth of generative models means that these are the main techniques used in the proposed methods. This paper aims to provide a comprehensive review of the deep learning-based techniques used in the field of video anomaly detection. Specifically, deep learning-based approaches have been categorized into different methods by their objectives and learning metrics. Additionally, preprocessing and feature engineering techniques are discussed thoroughly for the vision-based domain. This paper also describes the benchmark databases used in training and detecting abnormal human behavior. Finally, the common challenges in video surveillance are discussed, to offer some possible solutions and directions for future research.

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Section: Research Gapsmentioning

confidence: 99%

Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey

Duong

Hoang

2023

Sensors

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“…Anomaly detection is crucial in many fields and applications, including in healthcare [3][4][5][6], cybersecurity and computer networks with Fast Reroute mechanisms [7,8], financial industry [9][10][11][12], robotics [13][14][15], and video surveillance [16][17][18], among many others [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

GAN-Based Anomaly Detection Tailored for Classifiers

Králik,

Kontšek,

Škvarek

et al. 2024

Mathematics

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Pattern recognition systems always misclassify anomalies, which can be dangerous for uninformed users. Therefore, anomalies must be filtered out from each classification. The main challenge for the anomaly filter design is the huge number of possible anomaly samples compared with the number of samples in the training set. Tailoring the filter for the given classifier is just the first step in this reduction. Paper tests the hypothesis that the filter trained in avoiding “near” anomalies will also refuse the “far” anomalies, and the anomaly detector is then just a classifier distinguishing between “far real” and “near anomaly” samples. As a “far real” samples generator was used, a Generative Adversarial Network (GAN) fake generator that transforms normally distributed random seeds into fakes similar to the training samples. The paper proves the assumption that seeds unused in fake training will generate anomalies. These seeds are distinguished according to their Chebyshev norms. While the fakes have seeds within the hypersphere with a given radius, the near anomalies have seeds within the sphere near cover. Experiments with various anomaly test sets have shown that GAN-based anomaly detectors create a reliable anti-anomaly shield using the abovementioned assumptions. The proposed anomaly detector is tailored to the given classifier, but its limitation is due to the need for the availability of the database on which the classifier was trained.

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Transformer with Spatio-Temporal Representation for Video Anomaly Detection

Cited by 2 publications

References 18 publications

Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey

Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey

GAN-Based Anomaly Detection Tailored for Classifiers

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