Convolutional Neural Networks for Pedestrian Detection
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DenisTome Merge pull request #3 from spongezhang/master
Add model definition for Caffe which is used in RCNN
Latest commit 42d13ad Jun 17, 2016

README.md

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

  1. Prerequisites
  2. MATLAB (tested with 2015a on 64-bit Linux)
  3. Caffe's prerequisites
  4. Install Caffe and R-CNN
  5. Download Caffe (version described in R-CNN instructions)
  6. Download R-CNN and follow the instructions
  7. Install DeepPed
  8. Change into the R-CNN source code directory: cd rcnn
  9. Get the DeepPed source code by cloning the repository: git clone https://github.com/DenisTome/DeepPed.git
  10. Get the Piotr's Image & Video Matlab Toolbox by cloning the repository: git clone https://github.com/pdollar/toolbox.git
  11. From the R-CNN folder, run the model fetch script: ./DeepPed/fetch_models.sh.
  12. 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.
  1. Run the demo: >> deepPed_demo