Sermanet, P., Kavukcuoglu, K., Chintala, S. and Lecun, Y. (2013) Pedestrian Detection with Unsupervised Multi-Stage Feature Learning. 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, 23-28 June 2013, 3626-3633.
https://doi.org/10.1109/CVPR.2013.465

has been cited by the following article:

• TITLE: Research on Pedestrian Detection Technology Based on MSR and Faster R-CNN

  AUTHORS: Xueyun Zhao, Chaoju Hu

  KEYWORDS: Deep Learning, Pedestrian Detection, Region-Based Convolutional Neural Network, Image Enhancement, Non-Maximum Suppression

  JOURNAL NAME: Journal of Computer and Communications, Vol.6 No.7, July 30, 2018

  ABSTRACT: In order to avoid the problem of poor illumination characteristics and inaccurate positioning accuracy, this paper proposed a pedestrian detection algorithm suitable for low-light environments. The algorithm first applied the multi-scale Retinex image enhancement algorithm to the sample pre-processing of deep learning to improve the image resolution. Then the paper used the faster regional convolutional neural network to train the pedestrian detection model, extracted the pedestrian characteristics, and obtained the bounding boxes through classification and position regression. Finally, the pedestrian detection process was carried out by introducing the Soft-NMS algorithm, and the redundant bounding box was eliminated to obtain the best pedestrian detection position. The experimental results showed that the proposed detection algorithm achieves an average accuracy of 89.74% on the low-light dataset, and the pedestrian detection effect was more significant.