An Improved Faster R-CNN Pedestrian Detection Algorithm Based on Feature Fusion and Context Analysis
Considering the multi-scale and occlusion problem of pedestrian detection in natural scenes, we propose an improved Faster R-CNN pedestrian detection algorithm based on feature fusion and context analysis (FCF R-CNN). We design a feature fusion method of progressive cascade on VGG16 network, and add...
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doaj-d6aa8dc35222459fa896e84a12e3e61e2021-03-30T01:47:24ZengIEEEIEEE Access2169-35362020-01-01813811713812810.1109/ACCESS.2020.30125589151143An Improved Faster R-CNN Pedestrian Detection Algorithm Based on Feature Fusion and Context AnalysisSheping Zhai0https://orcid.org/0000-0001-8937-433XSusu Dong1https://orcid.org/0000-0002-1105-7075Dingrong Shang2https://orcid.org/0000-0002-4740-8068Shuhuan Wang3https://orcid.org/0000-0002-1699-3612School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, ChinaSchool of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, ChinaSchool of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, ChinaSchool of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, ChinaConsidering the multi-scale and occlusion problem of pedestrian detection in natural scenes, we propose an improved Faster R-CNN pedestrian detection algorithm based on feature fusion and context analysis (FCF R-CNN). We design a feature fusion method of progressive cascade on VGG16 network, and add LRN to speed up the convergence of the network. The improved feature extraction network enables our model to generate high-resolution feature maps containing rich, detailed and semantic information. We also adjust the RPN parameters to improve the proposal efficiency. In addition, we add a multi-layer iterative LSTM module to the detection model, which uses LSTM's memory ability to extract the global context information of the candidate boxes. This method only needs the feature map of the image itself as input, which highlights useful context information and enables the model to generate more accurate candidate boxes containing potential pedestrians. Our method performs better than existing methods in detecting small-size and occluded pedestrians, and has strong robustness in challenging scenes. Our method achieves competitive results in both accuracy and speed on Caltech pedestrian dataset, achieving a LAMR value of 36.75% and a runtime of 0.20 seconds per image. The validity of the algorithm has been proved.https://ieeexplore.ieee.org/document/9151143/Context analysisfaster R-CNNfeature fusionpedestrian detection |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sheping Zhai Susu Dong Dingrong Shang Shuhuan Wang |
spellingShingle |
Sheping Zhai Susu Dong Dingrong Shang Shuhuan Wang An Improved Faster R-CNN Pedestrian Detection Algorithm Based on Feature Fusion and Context Analysis IEEE Access Context analysis faster R-CNN feature fusion pedestrian detection |
author_facet |
Sheping Zhai Susu Dong Dingrong Shang Shuhuan Wang |
author_sort |
Sheping Zhai |
title |
An Improved Faster R-CNN Pedestrian Detection Algorithm Based on Feature Fusion and Context Analysis |
title_short |
An Improved Faster R-CNN Pedestrian Detection Algorithm Based on Feature Fusion and Context Analysis |
title_full |
An Improved Faster R-CNN Pedestrian Detection Algorithm Based on Feature Fusion and Context Analysis |
title_fullStr |
An Improved Faster R-CNN Pedestrian Detection Algorithm Based on Feature Fusion and Context Analysis |
title_full_unstemmed |
An Improved Faster R-CNN Pedestrian Detection Algorithm Based on Feature Fusion and Context Analysis |
title_sort |
improved faster r-cnn pedestrian detection algorithm based on feature fusion and context analysis |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Considering the multi-scale and occlusion problem of pedestrian detection in natural scenes, we propose an improved Faster R-CNN pedestrian detection algorithm based on feature fusion and context analysis (FCF R-CNN). We design a feature fusion method of progressive cascade on VGG16 network, and add LRN to speed up the convergence of the network. The improved feature extraction network enables our model to generate high-resolution feature maps containing rich, detailed and semantic information. We also adjust the RPN parameters to improve the proposal efficiency. In addition, we add a multi-layer iterative LSTM module to the detection model, which uses LSTM's memory ability to extract the global context information of the candidate boxes. This method only needs the feature map of the image itself as input, which highlights useful context information and enables the model to generate more accurate candidate boxes containing potential pedestrians. Our method performs better than existing methods in detecting small-size and occluded pedestrians, and has strong robustness in challenging scenes. Our method achieves competitive results in both accuracy and speed on Caltech pedestrian dataset, achieving a LAMR value of 36.75% and a runtime of 0.20 seconds per image. The validity of the algorithm has been proved. |
topic |
Context analysis faster R-CNN feature fusion pedestrian detection |
url |
https://ieeexplore.ieee.org/document/9151143/ |
work_keys_str_mv |
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