title: How can HOG be used to detect individual body parts
body: <h2>Information:</h2>
  
  <p>I would like to use OpenCV's HOG detection to identify objects that can be seen in a variety of orientations.  The only problem is, I can't seem to find a reasonable feature detector or classifier to detect this in a rotation and scale invaraint way (as is needed by objects such as forearms).  </p>
  
  <h2>Prior Work:</h2>
  
  <p>Lets focus on forearms for this discussion.  A forearm can have multiple orientations, the primary distinct features probably being its contour edges.  It is possible to have images of forearms that are pointing in any direction in an image, thus the complexity.  So far I have done some in depth research on using HOG descriptors to solve this problem, but I am finding that the variety of poses produced by forearms in my positives training set is producing very low detection scores in actual images.  I suspect the issue is that the gradients produced by each positive image do not produce very consistent results when saved into the Histogram.  I have reviewed many research papers on the topic trying to resolve or improvie this, including the original from Dalal &amp; Triggs [Link]: <a href="http://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf" rel="nofollow">http://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf</a> It also seems that the assumptions made for detecting whole humans do not necessary apply to detecting individual features (particularly the assumption that all humans are standing up seems to suggest HOG is not a good route for rotation invariant detection like that of forearms).  </p>
  
  <h2>Note:</h2>
  
  <p>If possible, I would like to steer clear of any non-free solutions such as those pertaining to Sift, Surf, or Haar.</p>
  
  <h2>Question:</h2>
  
  <p>What is a good solution to detecting rotation and scale invariant objects in an image?  Particularly for this example, what would be a good solution to detecting all orientations of forearms in an image?</p>