Variational Abnormal Behavior Detection With Motion Consistency

Li, Jing; Huang, Qingwang; Du, Yingjun; Zhen, Xiantong; Chen, Shengyong; Shao, Ling

doi:10.1109/tip.2021.3130545

Cited by 37 publications

(13 citation statements)

References 37 publications

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“…The method rates at a speed of 1,000-1,200 frames per second on average. Recently, Li et al [ 2022] adopt a motion loss to make the features of generated videos more consistent with the input videos.…”

Section: Supervised Methodsmentioning

confidence: 99%

A Survey of Visual Sensory Anomaly Detection

Jiang¹,

Xie²,

Wang³

et al. 2022

Preprint

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Visual sensory anomaly detection (AD) is an essential problem in computer vision, which is gaining momentum recently thanks to the development of AI for good. Compared with semantic anomaly detection which detects anomaly at the label level (semantic shift), visual sensory AD detects the abnormal part of the sample (covariate shift). However, no thorough review has been provided to summarize this area for the computer vision community. In this survey, we are the first one to provide a comprehensive review of visual sensory AD and category into three levels according to the form of anomalies. Furthermore, we classify each kind of anomaly according to the level of supervision. Finally, we summarize the challenges and provide open directions for this community. All resources are available at https://github.com/M-3LAB/awesomevisual-sensory-anomaly-detection.

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Section: Supervised Methodsmentioning

confidence: 99%

A Survey of Visual Sensory Anomaly Detection

Jiang¹,

Xie²,

Wang³

et al. 2022

Preprint

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show abstract

“…Sabokrou et al 6 used the fully convolutional layer of AlexNet to extract the deep features of the input video, and sending the features to cascaded Gaussian classifiers for anomaly detection. Li et al 7 introduced optical flow network to better predict temporal information, and anomaly detection through motion consistency. Alahi et al 8 proposed an unsupervised learning method to generate multiple LSTM networks for each pedestrian in a specific time frame, and predict the pedestrian's position at the current moment based on the pedestrian's historical position.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

“…Previous researchers have done a lot of work in abnormal event detection. According to the method of extracting features, abnormal event detection methods can be divided into two categories: traditional hand-crafted features method [1][2][3][4][5] and deep learning method [6][7][8][9][10][11] .…”

Section: Introductionmentioning

confidence: 99%

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Abnormal event detection based on appearance repair and motion consistency

Liu

2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

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The application of abnormal event detection in video surveillance is an active research field, but due to the imbalance of positive and negative samples in surveillance video, abnormal event detection is full of challenges. In this paper, we propose a new abnormal event detection method based on appearance repair and motion consistency for detecting anomalous events. Specifically, the input image is partially masked and then fed into our proposed appearance repair autoencoder for image reconstruction, and then the motion consistency of images is constructed by our proposed optical flow network. The experimental results on the UCSD, CUHK Avenue datasets show the superiority of the detection performance of our method.

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“…Xie et al [23] presented a deep learning algorithm to evaluate abnormal behavior based on spatiotemporal representation learning. Liu et al [24] adopted the framework of variational abnormal behavior detection to solve the variability of abnormal behavior coupled with huge ambiguity and uncertainty of video contents.…”

Section: Introductionmentioning

confidence: 99%

OL-JCMSR: A Joint Coding Monitoring Strategy Recommendation Model Based on Operation Log

Sun

Guo

et al. 2022

Mathematics

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A surveillance system with more than hundreds of cameras and much fewer monitors strongly relies on manual scheduling and inspections from monitoring personnel. A monitoring method which improves the surveillance performance by analyzing and learning from a large amount of manual operation logs is proposed in this paper. Compared to fixed rules or existing computer-vision methods, the proposed method can more effectively learn from the operators’ behaviors and incorporate their intentions into the monitoring strategy. To the best of our knowledge, this method is the first to apply a monitoring-strategy recommendation model containing a global encoder and a local encoder in monitoring systems. The local encoder can adaptively select important items in the operating sequence to capture the main purpose of the operator, while the global encoder is used to summarize the behavior of the entire sequence. Two experiments are conducted on two data sets. Compared with att-RNN and att-GRU, the joint coding model in experiment 1 improves the Recall@20 by 9.4% and 4.6%, respectively, and improves the MRR@20 by 5.49% and 3.86%, respectively. In experiment 2, compared with att-RNN and att-GRU, the joint coding model improves by 11.8% and 6.2% on Recall@20, and improves by 7.02% and 5.16% on MRR@20, respectively. The results illustrate the effectiveness of the our model in monitoring systems.

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Variational Abnormal Behavior Detection With Motion Consistency

Cited by 37 publications

References 37 publications

A Survey of Visual Sensory Anomaly Detection

A Survey of Visual Sensory Anomaly Detection

Abnormal event detection based on appearance repair and motion consistency

OL-JCMSR: A Joint Coding Monitoring Strategy Recommendation Model Based on Operation Log

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