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I am currently detecting heads in a CCTV image. I am using a HOG detector + SVM and I am using the sliding window technique to detect the heads. Of course, when I am scaling the image, I am having multiple detection/bounding boxes of the same head. I know that I have to use non-maxima suppression to choose the best one of them, and I have tried to follow the following link: http://quantombone.blogspot.com/2011/08/blazing-fast-nmsm-from-exemplar-svm.html
However, I cannot understand how to get the score for each sliding window. Can someone explain to me please? In other words, I have the bounding boxes pts and I know that I have to set an overlap of 0.5, but I do not have the score for each bounding box.
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Non Maximum Suppression is a computer vision method that selects a single entity out of many overlapping entities (for example bounding boxes in object detection). The criteria is usually discarding entities that are below a given probability bound.
The non-max suppression will first select the bounding box with the highest objectiveness score. And then remove all the other boxes with high overlap. So here, in the above image, We will select the Green bounding box for the dog (since it has the highest objectiveness score of 98%)
Non max suppression is a technique used mainly in object detection that aims at selecting the best bounding box out of a set of overlapping boxes. In the following image, the aim of non max suppression would be to remove the yellow, and blue boxes, so that we are left with only the green box.
Actually for non-maximum suppression you don't need the score associated to each bounding box. You can use the well-known NMS method of Viola and Jones (Boosted cascade of simple features):
And you have non-maximum suppression.
If you still want to use other routines that require output scores, then just assign to each bounding box the same score.
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