actions to deliver the final localization (i.e. bounding-box prediction) of the pedestrian instances. These actions come from a localization policy that is learned from data by exploiting both local contextual information and multi-layer representations using deep reinforcement learning techniques. Empirical evaluation reveals that the major ingredients in our approach, namely multi-layer representations, initial pedestrian proposals, and localization policy are all important in delivering the final results. Second, our approach works with pedestrian instances across scales in a more balanced manner, and performs particularly well with far-scale instances. Empirically it is examined on several widely-used benchmarks, including Caltech [@DolEtAl:cvpr09], ETH [@EssLeiGoo:iccv07], and TUD-Brussels [@WojWalSch:cvpr09] with consistently competitive results. For example, MS-CNN [@CaiEtAl:eccv16] performs among the best on Caltech benchmark. Compared to its state-of-the-art results, the log-average miss rate of our approach is further reduced by a very significant amount of 18.68% for far-scale pedestrians (below 80 pixels in bounding-box height), and a mild amount of 1.42% for near-scale pedestrians (80 or more). Similar results have also been observed for the other benchmarks. Third, our implementation and related results are made publicly available [^1] in support of the open-source activities of the research community.

![image](TIP_Fig2_architech_new.pdf){width="0.99\linewidth"}

Related Work {#sec:related work}
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There has 