Robust estimation of edge density in blurred images

Edge is an important cue for object detection in computer vision. In this paper, we present a filtering method for speeding up the object detection by using edge density as a prefiltering measure. Specifically the paper focuses on two problems of derivation of scale invariant edge density measure and robust edge extraction in blurred images. Normalization of edge density is performed based on the square root of the target area for scale invariance. Experimental result confirms validity of the suggested density measure. Second problem of edge extraction in blurred images is addressed by extracting edge pixels in scaled-down images with histogram equalization, giving more reliable edge extraction result. Experiment results on large set of pedestrian images captured under various conditions including daylight, raining, motion blur, and night are presented and analyzed quantitatively.