STAN: Spatio-Temporal Adversarial Networks for Abnormal Event Detection

Lee, Sangmin; Kim, Hak Gu; Ro, Yong Man

doi:10.1109/icassp.2018.8462388

Cited by 66 publications

(56 citation statements)

References 20 publications

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“…4. As the results show, we surpass all other GAN-based approaches and achieve better results than the methods of [11] and [12], which use a longer time span and thus a larger number of consecutive frames for detecting anomalies. Table 4: AUCs of GAN based anomaly detection methods for Ped2.…”

Section: Extensionsmentioning

confidence: 79%

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What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks

Golda¹,

Murzyn²,

Qu³

et al. 2019

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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Anomaly detection plays in many fields of research, along with the strongly related task of outlier detection, a very important role. Especially within the context of the automated analysis of video material recorded by surveillance cameras, abnormal situations can be of very different nature. For this purpose this work investigates Generative-Adversarial-Network-based methods (GAN) for anomaly detection related to surveillance applications. The focus is on the usage of static camera setups, since this kind of camera is one of the most often used and belongs to the lower price segment. In order to address this task, multiple subtasks are evaluated, including the influence of existing optical flow methods for the incorporation of short-term temporal information, different forms of network setups and losses for GANs, and the use of morphological operations for further performance improvement. With these extension we achieved up to 2.4% better results. Furthermore, the final method reduced the anomaly detection error for GANbased methods by about 42.8%.

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Section: Extensionsmentioning

confidence: 79%

“…work [11,12,17,18], anomaly scores are normalized videowise. Since for each sample two heat maps based on the different domains are obtained, fusion as proposed in [17] was applied.…”

Section: O P T I C a L F L O W P R E D I C T I O N 1 A N O M A Ly D Ementioning

confidence: 99%

What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks

Golda¹,

Murzyn²,

Qu³

et al. 2019

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

View full text Add to dashboard Cite

show abstract

“…A ROC curve is plotting true positive rate (TPR) as shown in Eq. (14), which is referred to as sensitivity, against false positive rate (FPR) as shown in Eq. (15), which is referred to specificity.…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

“…Finally, the fall is detected using jointly decision of the result of the three CNNs. In [14], the novel generative model based abnormal event detection method (STAN) is proposed. It consists of the spatio-temporal generator and the spatiotemporal discriminator.…”

Section: Related Workmentioning

confidence: 99%

A New Approach for Thermal Vision based Fall Detection Using Residual Autoencoder

Elshwemy¹,

Elbasiony²,

Saidahmed³

2020

IJIES

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This paper focuses on falling of the elderly people which is considered as one of the most critical issue that can face them in their life. To deal with such issue, we propose a new approach named a Spatio-temporal Residual AutoEncoder (SRAE) model. This model is an unsupervised fall detector based on utilizing the deep learning technique to detect falls of the elderly people. Our proposed model uses autoencoder based on convolutional neural network, convolutional long short term memory (ConvLSTM) network, and residual connections to extract spatial and temporal features of videos captured from thermal cameras. The reconstruction error of an autoencoder is used to detect falls recorded in such thermal videos. Furthermore, SRAE model is tested on the publicly available thermal dataset where thermal images conserve the privacy of the elderly under observation which is a very important issue. The obtained results show that the our proposed model detects falls with high receiver operating characteristic area under curve (ROC AUC) (97%) ,and precision recall area under curve (PR AUC) (93%) compared to denoising autoencoder (DAE), convolutional autoencoder (CAE), and convolutional long short term memory autoencoder (CLSTMAE) introduced in the literature.

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“…The effectiveness of the proposed framework was tested by comparing our model with six different approaches based on autoencoder. These are Con-vAE [12], ST-AE [2], ConvLSTM-AE [40], Two-Stream R-ConvVAE [41] and WCAE-LSTM [25], and STAN [42]. ConvAE [12] benefits from both fully connected autoencoder with trajectory-based handcrafted spatio-temporal features and convolutional autoencoder.…”

Section: Resultsmentioning

confidence: 99%

Anomaly Detection in Videos Using Optical Flow and Convolutional Autoencoder

Duman

Erdem

2019

IEEE Access

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Today, public areas, such as airports, hospitals, city centers are monitored by surveillance systems. The widespread use of surveillance systems reduces security concerns while creating an amount of video data that cannot be examined by people in real-time. Therefore, the concept of automatic understanding of video activities has raised the standards of security camera systems. In this paper, we propose a framework (OF-ConvAE-LSTM) to detect anomalies using Convolutional Autoencoder and Convolutional Long Short-Term Memory in an unsupervised manner. Besides the deep learning model, the feature extraction stage based on dense optical flow is applied in the framework to obtain the velocity and direction information of foreground objects. The experiments were carried out on three popular public datasets consisting of Avenue, UCSD Ped1, and UCSD Peds2. The experimental results have shown that the proposed framework models the complex distribution of the pattern of regular motion changes with high accuracy. Besides, this method was observed to outperform state-of-the-art approaches based on unsupervised and semi-supervised deep learning models.INDEX TERMS Abnormal event detection, convolutional autoencoder, long short-term memory, optical flow.

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STAN: Spatio-Temporal Adversarial Networks for Abnormal Event Detection

Cited by 66 publications

References 20 publications

What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks

What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks

A New Approach for Thermal Vision based Fall Detection Using Residual Autoencoder

Anomaly Detection in Videos Using Optical Flow and Convolutional Autoencoder

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