CCTV Image Sequence Generation and Modeling Method for Video Anomaly Detection Using Generative Adversarial Network

2018 ◽  
pp. 457-467 ◽  
Author(s):  
Wonsup Shin ◽  
Sung-Bae Cho
Keyword(s):  
Anomaly Detection ◽  
Image Sequence ◽  
Modeling Method ◽  
Generative Adversarial Network ◽  
Sequence Generation ◽  
Adversarial Network

NM-GAN: Noise-modulated generative adversarial network for video anomaly detection

2021 ◽  
Vol 116 ◽  
pp. 107969
Author(s):  
Dongyue Chen ◽  
Lingyi Yue ◽  
Xingya Chang ◽  
Ming Xu ◽  
Tong Jia
Keyword(s):  
Anomaly Detection ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals Spatio-temporal silhouette sequence reconstruction for gait recognition against occlusion

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Md. Zasim Uddin ◽  
Daigo Muramatsu ◽  
Noriko Takemura ◽  
Md. Atiqur Rahman Ahad ◽  
Yasushi Yagi
Keyword(s):  
Gait Cycle ◽  
Gait Recognition ◽  
Real Life ◽  
Image Sequence ◽  
Body Parts ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Hinge Loss ◽  
Sequence Reconstruction ◽  
Spatio Temporal

AbstractGait-based features provide the potential for a subject to be recognized even from a low-resolution image sequence, and they can be captured at a distance without the subject’s cooperation. Person recognition using gait-based features (gait recognition) is a promising real-life application. However, several body parts of the subjects are often occluded because of beams, pillars, cars and trees, or another walking person. Therefore, gait-based features are not applicable to approaches that require an unoccluded gait image sequence. Occlusion handling is a challenging but important issue for gait recognition. In this paper, we propose silhouette sequence reconstruction from an occluded sequence (sVideo) based on a conditional deep generative adversarial network (GAN). From the reconstructed sequence, we estimate the gait cycle and extract the gait features from a one gait cycle image sequence. To regularize the training of the proposed generative network, we use adversarial loss based on triplet hinge loss incorporating Wasserstein GAN (WGAN-hinge). To the best of our knowledge, WGAN-hinge is the first adversarial loss that supervises the generator network during training by incorporating pairwise similarity ranking information. The proposed approach was evaluated on multiple challenging occlusion patterns. The experimental results demonstrate that the proposed approach outperforms the existing state-of-the-art benchmarks.

scholarly journals Anomaly Detection in Particulate Matter Sensor Using Hypothesis Pruning Generative Adversarial Network

2020 ◽  
Author(s):  
Yeong Hyeon Park ◽  
Won Seok Park ◽  
Yeong Beom Kim
Keyword(s):  
Particulate Matter ◽  
Cost Effectiveness ◽  
Anomaly Detection ◽  
World Health Organization ◽  
Human Health ◽  
World Health ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Health Organization ◽  
Tapered Element Oscillating Microbalance

World Health Organization (WHO) provides the guideline for managing the Particulate Matter (PM) level because when the PM level is higher, it threats the human health. For managing PM level, the procedure for measuring PM value is needed firstly. We use Tapered Element Oscillating Microbalance (TEOM)-based PM measuring sensors because it shows higher cost-effectiveness than Beta Attenuation Monitor (BAM)-based sensor. However, TEOM-based sensor has higher probability of malfunctioning than BAM-based sensor. In this paper, we call the overall malfunction as an anomaly, and we aim to detect anomalies for the maintenance of PM measuring sensors. We propose a novel architecture for solving the above aim that named as Hypothesis Pruning Generative Adversarial Network (HP-GAN). We experimentally compare the several anomaly detection architectures to certify ours performing better.

scholarly journals Semi-supervised generative adversarial networks for anomaly detection

2022 ◽  
Vol 132 ◽  
pp. 01016
Author(s):  
Juan Montenegro ◽  
Yeojin Chung
Keyword(s):  
Anomaly Detection ◽  
Video Surveillance ◽  
Daily Life ◽  
Generative Adversarial Networks ◽  
Gamma Correction ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Different Types ◽  
Better Than

Advancements in security have provided ways of recording anomalies of daily life through video surveillance. For the present investigation, a semi-supervised generative adversarial network model to detect and classify different types of crimes on videos. Additionally, we intend to tackle one of the most recurring difficulties of anomaly detection: illumination. For this, we propose a light augmentation algorithm based on gamma correction to help the semi-supervised generative adversarial networks on its classification task. The proposed process performs slightly better than other proposed models.

scholarly journals Seismic Inverse Modeling Method based on Generative Adversarial Network

2021 ◽  
Author(s):  
Pengfei Xie ◽  
Yinshu Yin ◽  
Jiagen Hou ◽  
Mei Chen ◽  
Lixin Wang
Keyword(s):  
Inverse Modeling ◽  
Modeling Method ◽  
Generative Adversarial Network ◽  
Adversarial Network
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