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Proceedings Paper

Feature optimization for pedestrian detection based on faster R-CNN
Author(s): Mengxue Ren; Shuhua Lu
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Paper Abstract

Due to the rise of convolutional neural network, pedestrian detection has achieved great success. However, the features of many existing methods are not fully utilized, which results in unsatisfactory detection results. A new pedestrian detection model which is named of SE-Faster R-CNN is proposed in this paper. By adding SENet block to Faster RCNN, it can strengthen the expressive force of feature. Then, the GN-Faster R-CNN, which is generated by adding the normalization layer -- Group Normalization layer to Faster R-CNN, is proposed. The proposed architecture is trained and tested on Caltech dataset. In addition, VGG16 model and ZF model are used as the backbone structure of detection network. A comparative experiment is implemented to compare the effectiveness of the two optimization methods. It can be seen from the experimental results that, after adding SENet, the miss rates of ZF model and VGG16 model were reduced by 0.392% and 0.999%, respectively. After adding the GN layer, the miss rate of VGG16 model was reduced by 0.665%, while the miss rate of ZF model was increased by 2.093%.

Paper Details

Date Published: 27 November 2019
PDF: 7 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113210L (27 November 2019); doi: 10.1117/12.2541708
Show Author Affiliations
Mengxue Ren, People's Public Security Univ. of China (China)
Shuhua Lu, People's Public Security Univ. of China (China)

Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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