Abstract The serious performance decline with decreasing resolu-tion is the major bottleneck for current pedestrian detectiontechniques [14, 23]. In this paper, we take pedestrian de-tection in different resolutions as different but related prob-lems, and propose a Multi-Task model to jointly considertheir commonness and differences. The model contains resolution aware transformations to map pedestrians in different resolutions to a common space, where a shared detectoris constructed to distinguish pedestrians from background.For model learning, we present a coordinate descent procedure to learn the resolution aware transformations and deformable part model (DPM) based detector iteratively. In traffic scenes, there are many false positives located around vehicles, therefore, we further build a context model to suppress them according to the pedestrian-vehicle relationship. The context model can be learned automatically even when the vehicle annotations are not available. Our method reduces the mean miss rate to 60% for pedestrians taller than 30 pixels on the Caltech Pedestrian Benchmark, which noticeably outperforms previous state-of-the-art (71%).