Feature optimization for pedestrian detection based on faster R-CNN

Mengxue Ren; Shuhua Lu

doi:10.1117/12.2541708

27 November 2019 Feature optimization for pedestrian detection based on faster R-CNN

Mengxue Ren, Shuhua Lu

Proceedings Volume 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence; 113210L (2019) https://doi.org/10.1117/12.2541708
Event: The Second International Conference on Image, Video Processing and Artifical Intelligence, 2019, Shanghai, China

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%.

Citation Download Citation

Mengxue Ren and Shuhua Lu "Feature optimization for pedestrian detection based on faster R-CNN", Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113210L (27 November 2019); https://doi.org/10.1117/12.2541708

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KEYWORDS

Performance modeling

Data modeling

Convolutional neural networks

Feature extraction

Visualization

Convolution

Data conversion

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