CAFN: The Combination of Atrous and Fractionally Strided Convolutional Neural Networks for Understanding the Densely Crowded Scenes

2018 ◽  
pp. 297-307
Author(s):  
Lvyuan Fan ◽  
Minglei Tong ◽  
Min Li
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Crowded Scenes

Spatial–temporal convolutional neural networks for anomaly detection and localization in crowded scenes

2016 ◽  
Vol 47 ◽  
pp. 358-368 ◽  
Author(s):  
Shifu Zhou ◽  
Wei Shen ◽  
Dan Zeng ◽  
Mei Fang ◽  
Yuanwang Wei ◽  
...  
Keyword(s):  
Neural Networks ◽  
Anomaly Detection ◽  
Convolutional Neural Networks ◽  
Detection And Localization ◽  
Crowded Scenes

scholarly journals Crowd Counting in Low-Resolution Crowded Scenes Using Region-Based Deep Convolutional Neural Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 35317-35329 ◽  
Author(s):  
Muhammad Saqib ◽  
Sultan Daud Khan ◽  
Nabin Sharma ◽  
Michael Blumenstein
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Low Resolution ◽  
Deep Convolutional Neural Networks ◽  
Crowd Counting ◽  
Crowded Scenes

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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