A comprehensive review on deep learning-based methods for video anomaly detection

Nayak, Rashmiranjan; Pati, Umesh Chandra; Das, Santos Kumar

doi:10.1016/j.imavis.2020.104078

Cited by 175 publications

(85 citation statements)

References 77 publications

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“…Esta seçãoé uma breve revisão de alguns trabalhos diretamente relacionados com este, com o objetivo de contextualizar a presente pesquisa na literatura. Artigos de revisão (surveys) recentes que expandem a cobertura desta seção podem ser encontrados em [Suarez and Naval Jr 2020] e [Nayak et al 2020].…”

Section: Trabalhos Relacionadosunclassified

Uma Abordagem baseada em Redes Neurais, Multiple Instance Learning e PCA para Detecção de Anomalias em Videovigilância

Pereira¹,

Maia²

2021

Anais Do XLVIII Seminário Integrado De Software E Hardware (SEMISH 2021)

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Multiple Instance Learning (MIL) tem se tornado uma solução atrativa na literatura de videovigilância por permitir lidar com bases fracamente rotuladas. Este trabalho propõe e avalia uma abordagem para detecção de anomalias em vídeo baseada em classificação binária com redes neurais Multilayer Perceptron (MLP) e paradigma MIL. Os experimentos foram conduzidos a partir de um conjunto de atributos I3D (Inflated 3D) referentes ao dataset de benchmark ShanghaiTech. Explora-se ainda o efeito da compacticidade dos dados e representação de informação essencial com a técnica de extração de atributos Principal Component Analysis (PCA). Os resultados alcançados foram competitivos quando comparados com o estado da arte.

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Section: Trabalhos Relacionadosunclassified

Uma Abordagem baseada em Redes Neurais, Multiple Instance Learning e PCA para Detecção de Anomalias em Videovigilância

Pereira¹,

Maia²

2021

Anais Do XLVIII Seminário Integrado De Software E Hardware (SEMISH 2021)

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“…Instead, most of them perform surveys by targeting only specific functionality. However, a study conducted by Nayak et al [5] shows the advancement in video anomaly detection using deep learning techniques. The authors present the various deep learning techniques for video processing to detect the anomalies such as abnormal activities-fighting, riots, traffic rule violations, stampede, and strange entities -weapons, abandoned luggage, etc.…”

Section: A Literature Surveymentioning

confidence: 99%

“…The main techniques of deep learning are grouped into Convolutional Neural Network(CNN), Autoencoders (AEs), and Recurrent Neural Network (RNN). Another survey-based on anomaly detection from video data by [8] focuses deep learning approach where the author listed generative adversarial networks (GANs) along with other deep learning approaches mentioned in [5]. A significant application of video processing in computer-vision research is pedestrian detection.…”

Section: A Literature Surveymentioning

confidence: 99%

Video Processing Using Deep Learning Techniques: A Systematic Literature Review

et al. 2021

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Studies show lots of advanced research on various data types such as image, speech, and text using deep learning techniques, but nowadays, research on video processing is also an emerging field of computer vision. Several surveys are present on video processing using computer vision deep learning techniques, targeting specific functionality such as anomaly detection, crowd analysis, activity monitoring, etc. However, a combined study is still unexplored. This paper aims to present a Systematic Literature Review (SLR) on video processing using deep learning to investigate the applications, functionalities, techniques, datasets, issues, and challenges by formulating the relevant research questions (RQs). This systematic mapping includes 93 research articles from reputed databases published between 2011 and 2020. We categorize the deep learning technique for video processing as CNN, DNN, and RNN based. We observe the significant advancements in video processing between 2017 and 2020, primarily due to the advent of AlexNet, ResNet, and LSTM based deep learning techniques. The prominent fields of video processing research are observed as human action recognition, crowd anomaly detection, and behavior analysis. This SLR is a helpful guide for the researchers to explore the recent literature, available datasets, and existing deep learning techniques for video processing.

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“…With the increasing use of video surveillance, video anomaly detection has become an important task. Due to the fact that video anomalies are unbounded, rare, equivocal, irregular in real applications [2], video anomaly detection is challenging, and the problem is hard to be tackled with classification methods. Thus, deep-learning-based semi-supervised anomaly detection methods have been proposed and achieved significant improvements.…”

Section: Introductionmentioning

confidence: 99%

TransAnomaly: Video Anomaly Detection Using Video Vision Transformer

Cai²,

Zhou

et al. 2021

IEEE Access

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Video anomaly detection is challenging because abnormal events are unbounded, rare, equivocal, irregular in real scenes. In recent years, transformers have demonstrated powerful modelling abilities for sequence data. Thus, we attempt to apply transformers to video anomaly detection. In this paper, we propose a prediction-based video anomaly detection approach named TransAnomaly. Our model combines the U-Net and the Video Vision Transformer (ViViT) to capture richer temporal information and more global contexts. To make full use of the ViViT for the prediction, we modified the ViViT to make it capable of video prediction. Experiments on benchmark datasets show that the addition of the transformer module improves the anomaly detection performance. In addition, we calculate regularity scores with sliding windows and evaluate the impact of different window sizes and strides. With proper settings, our model outperforms other state-of-the-art prediction-based video anomaly detection approaches. Furthermore, our model can perform anomaly localization by tracking the location of patches with lower regularity scores.

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A comprehensive review on deep learning-based methods for video anomaly detection

Cited by 175 publications

References 77 publications

Uma Abordagem baseada em Redes Neurais, Multiple Instance Learning e PCA para Detecção de Anomalias em Videovigilância

Uma Abordagem baseada em Redes Neurais, Multiple Instance Learning e PCA para Detecção de Anomalias em Videovigilância

Video Processing Using Deep Learning Techniques: A Systematic Literature Review

TransAnomaly: Video Anomaly Detection Using Video Vision Transformer

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