The Method of Classifying Fog Level of Outdoor Video Images Based on Convolutional Neural Networks

2021 ◽  
Author(s):  
Xiangwei Zhao ◽  
Jiaojiao Jiang ◽  
Kang Feng ◽  
Bo Wu ◽  
Jishan Luan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Video Images

scholarly journals Classifying Behaviours in Videos with Recurrent Neural Networks

2017 ◽  
Vol 7 (4) ◽  
pp. 1-15 ◽  
Author(s):  
Javier Abellan-Abenza ◽  
Alberto Garcia-Garcia ◽  
Sergiu Oprea ◽  
David Ivorra-Piqueres ◽  
Jose Garcia-Rodriguez
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Video Surveillance ◽  
Activity Recognition ◽  
Human Activity ◽  
Recurrent Neural Networks ◽  
Low Cost ◽  
Video Images ◽  
Employee Monitoring

This article describes how the human activity recognition in videos is a very attractive topic among researchers due to vast possible applications. This article considers the analysis of behaviors and activities in videos obtained with low-cost RGB cameras. To do this, a system is developed where a video is input, and produces as output the possible activities happening in the video. This information could be used in many applications such as video surveillance, disabled person assistance, as a home assistant, employee monitoring, etc. The developed system makes use of the successful techniques of Deep Learning. In particular, convolutional neural networks are used to detect features in the video images, meanwhile Recurrent Neural Networks are used to analyze these features and predict the possible activity in the video.

scholarly journals Detecting gastric cancer from video images using convolutional neural networks

2018 ◽  
Vol 31 (2) ◽  
Author(s):  
Mitsuaki Ishioka ◽  
Toshiaki Hirasawa ◽  
Tomohiro Tada
Keyword(s):  
Gastric Cancer ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Video Images

scholarly journals Classifying Behaviours in Videos with Recurrent Neural Networks

2020 ◽  
pp. 965-980
Author(s):  
Javier Abellan-Abenza ◽  
Alberto Garcia-Garcia ◽  
Sergiu Oprea ◽  
David Ivorra-Piqueres ◽  
Jose Garcia-Rodriguez
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Video Surveillance ◽  
Activity Recognition ◽  
Human Activity ◽  
Recurrent Neural Networks ◽  
Low Cost ◽  
Video Images ◽  
Employee Monitoring

This article describes how the human activity recognition in videos is a very attractive topic among researchers due to vast possible applications. This article considers the analysis of behaviors and activities in videos obtained with low-cost RGB cameras. To do this, a system is developed where a video is input, and produces as output the possible activities happening in the video. This information could be used in many applications such as video surveillance, disabled person assistance, as a home assistant, employee monitoring, etc. The developed system makes use of the successful techniques of Deep Learning. In particular, convolutional neural networks are used to detect features in the video images, meanwhile Recurrent Neural Networks are used to analyze these features and predict the possible activity in the video.

Traffic Lights Detection in adverse conditions using Convolutional Neural Networks

2018 ◽  
Author(s):  
George Symeonidis ◽  
Peter P. Groumpos ◽  
Evangelos Dermatas
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Lights ◽  
Adverse Conditions

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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