Best way to train a pedestrian detector using dlib
<p>I am trying to train a pedestrian detector using <a href="http://dlib.net/" rel="nofollow">dlib</a> and the the <a href="http://pascal.inrialpes.fr/data/human/" rel="nofollow">INRIA Person Dataset</a>. </p>

<p>So far I used 27 images, the training is fast but the results are unsatisfying (on other images pedestrians are rarely recognized). Here is the result of my training using the train_object_detector program that comes with dlib (in /exmaples directory) :</p>

<blockquote>
  <p>Saving trained detector to object_detector.svm</p>
  
  <p>Testing detector on training data...</p>
  
  <p>Test detector (precision,recall,AP):        1 0.653061 0.653061</p>
  
  <p>Parameters used:</p>
  
  <p>threads:                 4</p>
  
  <p>C:                       1</p>
  
  <p>eps:                     0.01</p>
  
  <p>target-size:             6400</p>
  
  <p>detection window width:  47</p>
  
  <p>detection window height: 137</p>
  
  <p>upsample this many times : 0</p>
</blockquote>

<p>I am aware that other images need to be added to the training in order to have better results but before doing that I want to be sure of the meaning of every parameter printed in the result (precision, recall, AP, c, eps, ...) I am also wondering if you have any recommandations regarding the training : what images to choose ? how many images are needed ? Do I need to annotate every object in the image ? Do I need to ignore some regions in the image ? ...</p>

<p>One last question, is there any trained detector (svm file) that I can use to compare my results ?</p>

<p>Thank you for your answers</p>