Nonlinear background filter to improve pedestrian detection

Abstract

In this paper, we propose a simple nonlinear filter which improves the detection of pedestrians walking in a video. We do so by first cumulating temporal gradient of moving objects into a motion history image (MHI). Then we apply to each frame of the video a motion-guided nonlinear filter whose goal is to smudge out background details while leaving untouched foreground moving objects. The resulting blurry-background image is then fed to a pedestrian detector. Experiments reveal that for a given miss rate, our motion-guided nonlinear filter can decrease the number of false positives per image (FPPI) by a factor of up to 26. Our method is simple, computationally light, and can be applied on a variety of videos to improve the performances of almost any kind of pedestrian detectors.