Adversarial 3D Convolutional Auto-Encoder for Abnormal Event Detection in Videos

Sun, Che; Jia, Yingmin; Song, Hao; Wu, Yuwei

doi:10.1109/tmm.2020.3023303

Cited by 24 publications

(11 citation statements)

References 44 publications

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“…To address these issues, [ 125 ] presents an adversarial attention-based auto-encoder network to detect anomalies. Such generative adversarial networks (GANs) aim to learn the spatiotemporal patterns and train the auto-encoder by using the de-noising reconstruction error and adversarial learning strategy to detect anomalies without supervision [ 22 , 111 , 113 , 128 , 165 ].…”

Section: Anomaly Detection Methodologies In Video Surveillancementioning

confidence: 99%

“…Dataset: ABU, San Diego, HYDICE; Parameters: ROC, computing time [ 125 ] Adversarial attention based, auto-encoder GAN Normal patterns are learnt through adversarial attention-based auto-encoder and anomaly is detected. Dataset: ShanghaiTech, Avenue, UCSD, Subway; Parameters: AUC EER [ 128 ] Adversarial 3D Conv,Autoencoder Spatiotemporal patterns are learnt using adversarial 3D Conv, Autoencoder to detect abnormal events in videos; Dataset: Subway, UCSD, Avenue, ShanghaiTech; Parameters: AUC/EER [ 157 ] Sparse reconstruction Sparsity-based reconstruction method is used with low rank property to determine abnormal events. Datasets: UCSD, Avenue; Parameters: ROC, AUC, EER [ 162 ] Sparsity-based method Abnormal event detection in traffic surveillance using low-rank sparse representation (CLSR).…”

Section: Anomaly Detection Methodologies In Video Surveillancementioning

confidence: 99%

“…GAN-based auto-encoder proposed in [ 125 ] produces reconstruction error and detects anomalous events by distinguishing them from the normal events [ 111 ]. Further, an adversarial learning strategy and denoising reconstruction error are used to train a 3D convolutional auto-encoder to discriminate abnormal events [ 128 ]…”

Section: Anomaly Detection Methodologies In Video Surveillancementioning

confidence: 99%

See 2 more Smart Citations

Anomaly detection using edge computing in video surveillance system: review

Patrikar

Parate

2022

Int J Multimed Info Retr

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The current concept of smart cities influences urban planners and researchers to provide modern, secured and sustainable infrastructure and gives a decent quality of life to its residents. To fulfill this need, video surveillance cameras have been deployed to enhance the safety and well-being of the citizens. Despite technical developments in modern science, abnormal event detection in surveillance video systems is challenging and requires exhaustive human efforts. In this paper, we focus on evolution of anomaly detection followed by survey of various methodologies developed to detect anomalies in intelligent video surveillance. Further, we revisit the surveys on anomaly detection in the last decade. We then present a systematic categorization of methodologies for anomaly detection. As the notion of anomaly depends on context, we identify different objects-of-interest and publicly available datasets in anomaly detection. Since anomaly detection is a time-critical application of computer vision, we explore the anomaly detection using edge devices and approaches explicitly designed for them. The confluence of edge computing and anomaly detection for real-time and intelligent surveillance applications is also explored. Further, we discuss the challenges and opportunities involved in anomaly detection using the edge devices.

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Section: Anomaly Detection Methodologies In Video Surveillancementioning

confidence: 99%

Section: Anomaly Detection Methodologies In Video Surveillancementioning

confidence: 99%

Section: Anomaly Detection Methodologies In Video Surveillancementioning

confidence: 99%

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Anomaly detection using edge computing in video surveillance system: review

Patrikar

Parate

2022

Int J Multimed Info Retr

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“…To cope with this, self-supervised signals are developed by extracting spatiotemporal patterns in videos and agglomerative clustering is employed to obtain a similarity relationship between the inputs to train C3D. Some studies have also proposed to used C3D and adversarial auto-encoder for detecting abnormal events in videos [55]. The 3D convolution auto-encoder model aims to learn the spatiotemporal patterns and train the auto-encoder by using the de-noising reconstruction error and adversarial learning strategy to detect anomalies without supervision [56].…”

Section: Unsupervised Learningmentioning

confidence: 99%

“…GAN based auto-encoder proposed in [53] produce reconstruction error and detect abnormal events by distinguishing them from the normal patterns. Further, an adversarial learning strategy and denoising reconstruction error are used to train a 3D convolutional auto-encoder to discriminate abnormal events [55].…”

Section: Reconstruction Basedmentioning

confidence: 99%

Anomaly Detection using Edge Computing in Video Surveillance System: Review

Patrikar¹,

Parate²

2021

Preprint

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The current concept of Smart Cities influences urban planners and researchers to provide modern, secured and sustainable infrastructure and give a decent quality of life to its residents. To fulfill this need video surveillance cameras have been deployed to enhance the safety and well-being of the citizens. Despite technical developments in modern science, abnormal event detection in surveillance video systems is challenging and requires exhaustive human efforts. In this paper, we surveyed various methodologies developed to detect anomalies in intelligent video surveillance. Firstly, we revisit the surveys on anomaly detection in the last decade. We then present a systematic categorization of methodologies developed for ease of understanding. Considering the notion of anomaly depends on context, we identify different objects-of-interest and publicly available datasets in anomaly detection. Since anomaly detection is considered a time-critical application of computer vision, our emphasis is on anomaly detection using edge devices and approaches explicitly designed for them. Further, we discuss the challenges and opportunities involved in anomaly detection at the edge.

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Intelligent abnormal behavior detection using double sparseness method

Sun

Chen

et al. 2022

Appl Intell

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Adversarial 3D Convolutional Auto-Encoder for Abnormal Event Detection in Videos

Cited by 24 publications

References 44 publications

Anomaly detection using edge computing in video surveillance system: review

Anomaly detection using edge computing in video surveillance system: review

Anomaly Detection using Edge Computing in Video Surveillance System: Review

Intelligent abnormal behavior detection using double sparseness method

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