Part-level fully convolutional networks for pedestrian detection

Xinran Wang; Cheolkon Jung; Alfred O. Hero III

ICASSP 2017accepted0 citations

Part-level fully convolutional networks for pedestrian detection

Xinran Wang, Cheolkon Jung, Alfred O. Hero III

Abstract

Since pedestrians in videos have a wide range of appearances such as body poses, occlusions, and complex backgrounds, pedestrian detection is a challengeable task. In this paper, we propose part-level fully convolutional networks (FCN) for pedestrian detection. We adopt deep learning to deal with the proposal shifting problem in pedestrian detection. First, we combine convolutional neural networks (CNN) and FCN to align bounding boxes for pedestrians. Then, we perform part-level pedestrian detection based on CNN to recall the lost body parts. Experimental results demonstrate that the proposed method achieves 6.83% performance improvement in log-average miss rate over CifarNet.

BibTeX

@inproceedings{icassp2017_partlevelfullyco,
  title = {Part-level fully convolutional networks for pedestrian detection},
  author = {Xinran Wang and Cheolkon Jung and Alfred O. Hero III},
  booktitle = {ICASSP 2017},
  year = {2017}
}