Bounding Boxing

A positive classification of a Detection Window [DW] outputs the information about its coordinates, as well as at which scale that detection was made. Knowing that the same object will be detected several times at different scales, a careful treatment of the raw outputed information is mandatory.

 

This is a representation of the untreated data. By looking at it we immediately figure out that the same objects are being detected multiple times at different scales, so the first step was to transform all the rectangles to the original scale:

 

Final step is to group the rectangles in terms of distance between each other. This is a clustering problem, and is an advanced procedure. Fortunately openCV already has a function that does this for us, and using the standard parameters this is the result:

This image in particular was chosen for this entry because of the good results it provided. This is not the case for the whole dataset, since my algorithm is prone to identify bike wheels, trees, and other objects as pedestrians, so my works is far from finished.

Regards.