An Improved Faster R-CNN Pedestrian Detection Algorithm Based on Feature Fusion and Context Analysis

IEEE Access

Sheping Zhai¹  Shuhuan Wang¹  Susu Dong²  Dingrong Shang³ 

[1] School of Computer Science and Technology, Xi&x2019;an University of Posts and Telecommunications, Xi&x2019;an, China;

关键词: Context analysis;    faster R-CNN;    feature fusion;    pedestrian detection;   

DOI  :  10.1109/ACCESS.2020.3012558

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown