| Summary: | Pedestrian detection attracts much attention from the academic community since it is an essential and significant component of autonomous driving. Despite many similarities with general object detection, pedestrian detection still has unique challenges such as background false positives and missed detections in a crowd. In this paper, we explore the value of contextual information for pedestrian detection, arguing that the contextual information is not a panacea in all cases. Despite the importance of enriching context, a method should focus on the core area where there is the target instance. To this end, we propose a pedestrian detection framework that extends proposals to enrich the context and relieves the confusions caused by contextual information through a supervised attention module. The ablation study demonstrates that the proposed extension is effective for both crowded and non-crowded instances; moreover, the attention module narrows the precision gap between these two cases. The extensive experiments on commonly used pedestrian benchmarks are conducted, which show the superior performance of our method. We achieve the average precision of 74.78% on KITTI pedestrian benchmark with the hard metric level, ranking the first places among all methods.
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