scholarly journals A Novel Approach to Measuring Urban Waterlogging Depth from Images Based on Mask Region-Based Convolutional Neural Network

2020 ◽  
Vol 12 (5) ◽  
pp. 2149 ◽  
Author(s):  
Jing Huang ◽  
Jinle Kang ◽  
Huimin Wang ◽  
Zhiqiang Wang ◽  
Tian Qiu
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Convolutional Neural Network ◽  
Risk Mitigation ◽  
Pythagorean Theorem ◽  
Reference Object ◽  
Surveillance Video ◽  
Traffic Surveillance ◽  
Novel Approach ◽  
Video Systems

Quickly obtaining accurate waterlogging depth data is vital in urban flood events, especially for emergency response and risk mitigation. In this study, a novel approach to measure urban waterlogging depth was developed using images from social networks and traffic surveillance video systems. The Mask region-based convolutional neural network (Mask R-CNN) model was used to detect tires in waterlogging, which were considered to be reference objects. Then, waterlogging depth was calculated using the height differences method and Pythagorean theorem. The results show that tires detected from images can been used as an effective reference object to calculate waterlogging depth. The Pythagorean theorem method performs better on images from social networks, and the height differences method performs well both on the images from social networks and on traffic surveillance video systems. Overall, the low-cost method proposed in this study can be used to obtain timely waterlogging warning information, and enhance the possibility of using existing social networks and traffic surveillance video systems to perform opportunistic waterlogging sensing.

scholarly journals VEHICLE CLASSIFICATION USING THE CONVOLUTION NEURAL NETWORK APPROACH

2021 ◽  
Vol 112 ◽  
pp. 201-209
Author(s):  
Janak TRIVEDI ◽  
Mandalapu Sarada DEVI ◽  
Dave DHARA
Keyword(s):  
Neural Network ◽  
Intelligent Transportation System ◽  
Convolution Neural Network ◽  
Vehicle Classification ◽  
Surveillance Video ◽  
Traffic Surveillance ◽  
Neural Network Approach ◽  
Video Systems ◽  
Manual Selection ◽  
Toll Collection

We present vehicle detection classification using the Convolution Neural Network (CNN) of the deep learning approach. The automatic vehicle classification for traffic surveillance video systems is challenging for the Intelligent Transportation System (ITS) to build a smart city. In this article, three different vehicles: bike, car and truck classification are considered for around 3,000 bikes, 6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract different vehicle dataset’s different features without a manual selection of features. The accuracy of CNN is measured in terms of the confidence values of the detected object. The highest confidence value is about 0.99 in the case of the bike category vehicle classification. The automatic vehicle classification supports building an electronic toll collection system and identifying emergency vehicles in the traffic.

Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

2020 ◽  
Vol 2 (2) ◽  
pp. 37-41
Author(s):  
Zhixian Chen ◽  
Jialin Tang ◽  
Xueyuan Gong ◽  
Qinglang Su
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Principal Component ◽  
Activation Function ◽  
Support Vector ◽  
Data Set ◽  
Novel Approach ◽  
Rectified Linear Unit ◽  
The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

Privacy Preserving in Video Surveillance Systems Using Regression Residual Convolutional Neural Network in Private and Public Places

2020 ◽  
Vol 12 (1) ◽  
pp. 39-55
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Video Surveillance ◽  
Security Policy ◽  
Surveillance Systems ◽  
Surveillance Video ◽  
Public Places ◽  
Private And Public ◽  
The One ◽  
Regression Residual

In recent years, surveillance video has become a familiar phenomenon because it gives us a feeling of greater security, but we are continuously filmed and our privacy is greatly affected. This work deals with the development of a private video surveillance system (PVSS) using regression residual convolutional neural network (RR-CNN) with the goal to propose a new security policy to ensure the privacy of no-dangerous person and prevent crime. The goal is to best meet the interests of all parties: the one who films and the one who is filmed.

scholarly journals A novel approach to reconstruction based saliency detection via convolutional neural network stacked with auto-encoder

2019 ◽  
Vol 349 ◽  
pp. 145-155 ◽  
Author(s):  
Xinchen Lin ◽  
Yang Tang ◽  
Huaglory Tianfield ◽  
Feng Qian ◽  
Weimin Zhong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Saliency Detection ◽  
Novel Approach
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