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Learning Cross-Modal Deep Representations for Robust Pedestrian Detection

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

<p align="center"> <img src="Teaser.jpg" width="600"/> </p> ## 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: <pre>@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} }</pre> ## Requirements <p> Please first download and install this modified caffe version for CMT-CNN, and test by downloading the trained model and network definition file from <a href="https://drive.google.com/drive/folders/0ByWGxNo3TouJNFRydFptVG5RWVkthk?usp=sharing">Google Drive</a>.</p>