RPNplus

This repository is not going to be updated anymore. The new detection model will be published here: TARTDetection

Code accompanying the paper "Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters(CVPR2017)". As for the generator for synthetic data, please take this repo for reference.

Requirement

ubuntu or Mac OS
tensorflow==1.1+
pip install image
pip install sklearn
pip install scipy
image_pylib(This repository should be put under the same folder with RPNplus.)

Usage

Run Demo:

Download model files(RPN_model & VGG16_model) first, and put them in the ./models/ folder.
The number 0 is your GPU index, and you can change to any available GPU index.
This demo will test the images in the ./images/ folder and output the results to ./results/ folder.

python demo.py 0

Train:

The number 0 is your GPU index, and you can change to any available GPU index.
Open train.py and set imageLoadDir and anoLoadDir to proper values(imageLoadDir means where you store your images and anoLoadDir means where you store your annotation files).

python train.py 0

Dataset Download

Related Datasets

MIKKI Dataset

Cite

Please cite our paper if you use this code or our datasets in your own work:

@InProceedings{Huang_2017_CVPR,
author = {Huang, Shiyu and Ramanan, Deva},
title = {Expecting the Unexpected: Training Detectors for Unusual Pedestrians With Adversarial Imposters},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}

Acknowledgement

Our code is based on Yinpeng Dong's code and this repo: https://github.com/machrisaa/tensorflow-vgg

Author

Shiyu Huang(huangsy1314@163.com)

huangshiyu13/RPNplus