Mtaiana: Created page with "===Discriminatively Trained Deformable Part Models=== Felzenszwalb, Girshick, McAllester and Ramanan released their code for part-based object detection, for which the won the VO..."

2011-11-02T15:32:44Z

Created page with "===Discriminatively Trained Deformable Part Models=== Felzenszwalb, Girshick, McAllester and Ramanan released their code for part-based object detection, for which the won the VO..."

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===Discriminatively Trained Deformable Part Models===
Felzenszwalb, Girshick, McAllester and Ramanan released their code for part-based object detection, for which the won the VOC "Lifetime Achievement" Prize.<br>
You can download version 4 of the code [http://www.cs.brown.edu/~pff/latent/ here].<br>
You can download the code plus some data for learning a star cascade (faster than the normal model?) [http://people.cs.uchicago.edu/~rbg/star-cascade/ here].

==Installing==
I tried to run the main software on Ubuntu 11.04/Matlab 2010a/gcc 5.4.2, but I got the following warning and error (plus others):
You are using gcc version "4.5.2-8ubuntu4)". The version currently supported with MEX is "4.2.3".

/usr/lib/x86_64-linux-gnu/gcc/x86_64-linux-gnu/4.5.2/cc1plus: /opt/MatlabR2010a/sys/os/glnxa64/libstdc++.so.6: version `GLIBCXX_3.4.14' not found (required by /usr/lib/libppl_c.so.2)

For fixing this I needed to change some of the libraries Matlab links to when building mex files:
* make a backup copy of the directory $MATLAB/sys/os/glnxa64
cd /opt/MatlabR2010a/sys/os/
sudo cp -r glnxa64 ~
* remove two files from that directory
cd glnxa64
sudo rm libstdc++.so.6
sudo rm libstdc++.so.6.0.9
* substitute the two files with links to the version currently in use on the system
sudo ln -s /usr/lib/x86_64-linux-gnu/libstdc++.so.6* .

==Running==
You can test the detector by running the '''demo.m''' file, or you can test the pedestrian detector on any image like this:

close all, clear;

load('INRIA/inriaperson_final'); %load the model
im=imread('000061.jpg'); %load the image

[dets, boxes] = imgdetect(im, model, -0.3); %perform the detection

%post process output: only show bounding boxes (no parts) and apply non-maximum suppression
bbox = bboxpred_get(model.bboxpred, dets, reduceboxes(model, boxes));
bbox = clipboxes(im, bbox);
top = nms(bbox, 0.5);
showboxes(im, bbox(top,:));

FelzenszwalbDetectionCode - Revision history

Mtaiana: Created page with "===Discriminatively Trained Deformable Part Models=== Felzenszwalb, Girshick, McAllester and Ramanan released their code for part-based object detection, for which the won the VO..."