  An image pyramid can extend many object detection algorithms to solve
detection on multiple scales. However, interpolation during the resampling
process of an image pyramid causes gradient variation, which is the difference
of the gradients between the original image and the scaled images. Our key
insight is that the increased variance of gradients makes the classifiers have
difficulty in correctly assigning categories. We prove the existence of the
gradient variation by formulating the ratio of gradient expectations between an
original image and scaled images, then propose a simple and novel gradient
normalization method to eliminate the effect of this variation. The proposed
normalization method reduce the variance in an image pyramid and allow the
classifier to focus on a smaller coverage. We show the improvement in three
different visual recognition problems: pedestrian detection, pose estimation,
and object detection. The method is generally applicable to many vision
algorithms based on an image pyramid with gradients.
