Skip to content
Learning Cross-Modal Deep Representations for Robust Pedestrian Detection
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
cmake
data
docs
examples
include/caffe
matlab
models
python
scripts
src
tools
windows/caffe
.DS_Store
CMakeLists.txt
CONTRIBUTORS.md
INSTALL.md
LICENSE
Makefile
Makefile.config.example
README.md
caffe.cloc

README.md

Learning Cross-Modal Deep Representations for Robust Pedestrian Detection

By Dan Xu, Wanli Ouyang, Elisa Ricci, Xiaogang Wang and Nicu Sebe

Introduction

CMT-CNN is a pedestrian detection approach asscoiated to an arxiv submission https://arxiv.org/abs/1704.02431 which is accepted at CVPR 2017. The code is implemented with Caffe and has been tested under the configurations of Ubuntu 14.04, MATLAB 2015b and CUDA 8.0.

Cite CMT-CNN

Please consider citing our paper if the code is helpful in your research work:

@inproceedings{xu2017learning,
  title={Learning Cross-Modal Deep Representations for Robust Pedestrian Detection},
  author={Xu, Dan and Ouyang, Wanli and Ricci, Elisa and Wang, Xiaogang and Sebe, Nicu},
  journal={CVPR},
  year={2017}
}

Requirements

Please first download and install this modified caffe version for CMT-CNN, and test by downloading the trained model and network definition file from Google Drive.

You can’t perform that action at this time.