Facing the same question...do we have a clear answer now? According to the documentation "training a cascade object detector", "the function runs the detector consisting of the stages already trained on the negative images. Any objects detected from these image are false positives...". Let's assume we have the large image mentioned in your question above (4034x3024 pixel). The detector is 30x32 pixel. The "trainCascadeObjectDetector" includes the input argument "ObjectTraningSize". It says, "before training, the function resizes the positive and negative samples to ObjectTrainingSize in pixels", i.e. to 30x32 pixels. The negative image and the detector now have the same size, they perfectly fit on top of each other, right? Hence, the detector checks only once whether it can find the desired object or not. This would mean that large images should be cut into smaller tiles to improve the negative samples, as the detector does not slide over the whole large image. Is this correct?
And a second question: If we have 100 negative images in our negative database and the training says "using at most 50 negative samples per stage". Does it mean, that 50 negative images are randomly selected from the database, giving the chance to use the images 51-100, or does it just use the images 1-50 for the whole training?
Thank you!
