In testing an object detection algorithm in large images, we check our detected bounding boxes against the coordinates given for the ground truth rectangles.
According to the Pascal VOC challenges, there's this:
A predicted bounding box is considered correct if it overlaps more
than 50% with a ground-truth bounding box, otherwise the bounding box
is considered a false positive detection. Multiple detections are
penalized. If a system predicts several bounding boxes that overlap
with a single ground-truth bounding box, only one prediction is
considered correct, the others are considered false positives.
This means that we need to calculate the percentage of overlap. Does this mean that the ground truth box is 50% covered by the detected boundary box? Or that 50% of the bounding box is absorbed by the ground truth box?
I've searched but I haven't found a standard algorithm for this - which is surprising because I would have thought that this is something pretty common in computer vision. (I'm new to it). Have I missed it? Does anyone know what the standard algorithm is for this type of problem?
Answer
I found that the conceptual answer is here:
http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2012/htmldoc/devkit_doc.html#SECTION00054000000000000000
from this thread:
Compare two bounding boxes with each other Matlab
I should be able to code this in python!
No comments:
Post a Comment