DeepPed: Deep Convolutional Neural Networks for Pedestrian Detection

Created by Denis Tomè, Federico Monti, Luca Baroffio and Luca Bondi.

Introduction

DeepPed is a state-of-the-art pedestrian detector that extends R-CNN work done by Girshick et al. combining region proposals with rich features computed by a convolutional neural network. This method achieves 19.90% log-average-miss-rate on the Caltech Pedestrian Dataset.

DeepPed is described in an arXiv tech report and will appear in Elsevier Journal of Signal Processing.

Citing R-CNN

If you find R-CNN useful in your research, please consider citing:

@article{tome2015Deep,
    author = {Tomè, Denis and Monti, Federico and Baroffio, Luca and Bondi, Luca and Tagliasacchi, Marco and Tubaro, Stefano},
    title = {Deep convolutional neural networks for pedestrian detection},
    journal = {arXiv preprint arXiv:1510.03608},
    year = {2015}
}

}

License

DeepPed is released under the Simplified BSD License (refer to the LICENSE file for details).

Installing R-CNN

0. Prerequisites
1. MATLAB (tested with 2015a on 64-bit Linux)
2. Caffe's prerequisites
3. Install Caffe and R-CNN
4. Download Caffe (version described in R-CNN instructions)
5. Download R-CNN and follow the instructions
6. Install DeepPed
7. Change into the R-CNN source code directory: cd rcnn
8. Get the DeepPed source code by cloning the repository: git clone https://github.com/DenisTome/DeepPed.git
9. Get the Piotr's Image & Video Matlab Toolbox by cloning the repository: git clone https://github.com/pdollar/toolbox.git
10. From the R-CNN folder, run the model fetch script: ./DeepPed/fetch_models.sh.
11. Open the startup.m matlab file, adding the two commands addpath(genpath('DeepPed')); and addpath(genpath('toolbox')); at the end of the file.

Running DeepPed on an image

1. Change to where you installed R-CNN: cd rcnn.
2. Start MATLAB matlab.

• Important: if you don't see the message R-CNN startup done when MATLAB starts, then you probably didn't start MATLAB in rcnn directory.

3. Run the demo: >> deepPed_demo