---
author:
- 'Xiaowei Zhang, Li Cheng, Bo Li, and Hai-Miao Hu'
bibliography:
- 'objDet\_TIP17.bib'
title: |
    Too Far to See? Not Really!\
    — Pedestrian Detection with Scale-aware Localization Policy
---

detection has been an important computer vision research topic over the years, with a wide range of applications including video surveillance, intelligent vehicles, robotics, and human computer interaction, to name a few. Significant progress has been made recently [@ShaEtAl:cvpr16; @TiaEtAl:cvpr15; @YanEtAl:iccv15; @SerEtAl:cvpr13; @LiEtAl:cvpr15; @RedEtAl:cvpr16] in terms of both effectiveness [@ParEtAl:cvpr13; @PaiSheHen:iccv13; @PaiSheHen:eccv14; @GirEtAl:cvpr14; @ZhaBenSch:cvpr15; @CaoPanLi:tip16] and efficiency [@DolEtAl:bmvc10; @WalEtAl:cvpr10; @DolAppKie:eccv12; @CheEtAl:cvpr13; @OuyWan:iccv13; @OuyZenWan:cvpr13]. On the other hand, as presented in Fig. \[fig\_1\](a), a typical image often contains multiple pedestrians of different scales, and current detection performance varies significantly over scales: Although the state-of-the-art detectors typically work reasonably well with large size pedestrians where the objects are near the camera (i.e. near-scaled), their performance becomes considerably worse when dealing with small-sized (i.e. far-scaled) ones. It has been observed that the lack of satisfactory outcome toward detecting far-scale pedestrians is due to the following inherent challenges: First, comparing with near-scale pedestrian instances as shown in Fig. \[fig\_1\](c), far-scale ones often retain much less information, yet these instances contain a greater noise 