Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation

Edited by Chengyang Li, Zhejiang University.

Demo code of our paper Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation by Chengyang Li, Dan Song, Ruofeng Tong and Min Tang. BMVC 2018. [project link].

Demo

0. Prerequisites

　Basic Tensorflow and Python package installation.

　This code is tested on [Ubuntu14.04, tf1.2, Python2.7] and [Ubuntu16.04, tf1.11, Python3.5].

1. Clone the repository

git clone https://github.com/Li-Chengyang/MSDS-RCNN.git

2. Update your -arch in setup script to match your GPU

cd MSDS-RCNN/lib
# Change the GPU architecture (-arch) if necessary
vim setup.py

3. Build the Cython modules

make clean
make
cd ..

4. Download the pre-trained model

　VGG16 model [OneDrive] trained on KAIST using original training annotaions.

　VGG16 model [OneDrive] trained on KAIST using sanitized training annotaions.

# Untar files to output/
cd output
tar -xvf pretrained.tar
tar -xvf pretrained_sanitized.tar

5. Run demo

　Model pre-trained on the orignial training annotations

python tools/demo.py

　Model pre-trained on the sanitized training annotations

python tools/demo.py --dataset sanitized

Detection performance

Note: Since the original annotations of the test set contain many problematic bounding boxes, we use the improved testing annotations provided by Liu et al. to enable a reliable comparison.

Acknowledgements

Thanks to Xinlei Chen, this pipeline is largely built on his example tensorflow Faster R-CNN code available at: https://github.com/endernewton/tf-faster-rcnn

Citing our paper

If you find our work useful in your research, please consider citing:

@InProceedings{li_2018_BMVC,
  author = {Li, Chengyang and Song, Dan and Tong, Ruofeng and Tang, Min},
  title = {Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation},
  booktitle = {British Machine Vision Conference (BMVC)},
  month = {September}
  year = {2018}
}