Improving pedestrian detection using motion-guided filtering

Abstract

In this letter, we show how a simple motion-guided nonlinear filter can drastically improve the accuracy of several pedestrian detectors. More specifically, we address the problem of how to pre-filter an image so almost any pedestrian detector will see its false detection rate decrease. First, we roughly identify moving pixels by cumulating their temporal gradient into a motion history image (MHI). The MHI is then used in conjunction with a nonlinear filter to filter out background details while leaving untouched foreground moving objects. We also show how a feedback loop as well as a merging procedure between the filtered and the unfiltered frames can further improve results. We tested our method on 26 videos from 6 categories. The results show that for a given miss rate, filtering out background details reduces the false detection rate by a factor of up to 69.6 times. Our method is simple, computationally light, and can be implemented with any pedestrian detector. Code is made publicly available at: https://bitbucket.org/wany1601/pedestriandetection