This code produces the results presented in http://arxiv.org/abs/1409.5209 on the Caltech dataset (optical flow used; so it doesn't work on the INRIA dataset) with BING as the pre-processor.
If you use this code in your research, please cite our papers:
@inproceedings{PaisitkriangkraiSH14a,
author = {Sakrapee Paisitkriangkrai and
Chunhua Shen and
Anton {van den Hengel}},
title = {Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features},
booktitle = {Proc. European Conf. Comp. Vis.},
year = {2014},
ee = {http://arxiv.org/abs/1407.0786},
}
@inproceedings{PaisitkriangkraiSH14b,
author = {Sakrapee Paisitkriangkrai and
Chunhua Shen and
Anton van den Hengel},
title = {Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {2015},
ee = {http://arxiv.org/abs/1409.5209},
}
The current demo contains a few test images from Caltech Pedestrian data sets (set07, V004).
(a) Compile optical flow source code if needed by (Precompiled files provided already! You may not need to compile your own version)
sh> ./mex_optical.sh
matlab> demo
WARNING: It may take 2 to 4 hours to get the result, depending on your machine. You should see a plot as below.
http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/datasets/USA/
NB: set00-set05 are used for training and set06-set10 are used for testing see http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/
The data set is in seq video format. Download MATLAB functions for read/write seq video files from http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html
The code has been tested to run on Ubuntu 14.04LTS (kernel: Linux 3.13.0-39-generic #66-Ubuntu SMP x86_64 GNU/Linux), Matlab 2013a.