Skip to main content
eScholarship
Open Access Publications from the University of California

Pedestrian Detection Based on Deep Learning

  • Author(s): Chen, Li
  • Advisor(s): Zhu, Qi
  • et al.
Abstract

In general, researchers use hand-crafted methods or combine with the deep learning to solve the problem of Pedestrian Detection. In this paper, this problem can be implemented in the purely convolution neural network. Region Proposal Network, proposed by the algorithm for objects detection could be modified and applied on the pedestrian detection. After getting feature maps from the pretrained model, feed them into the new model and train by using Tensorflow as the deep learning framework, we can get the predicted bounding boxes that contain the pedestrians. This method is efficient and can reach the accuracy around 80 percent.

Main Content
Current View