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- W2557736598 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" @default.
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- W2557736598 date "2017-09-01" @default.
- W2557736598 modified "2023-10-18" @default.
- W2557736598 title "Improving pedestrian detection using motion-guided filtering" @default.
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- W2557736598 doi "https://doi.org/10.1016/j.patrec.2016.11.020" @default.
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