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To construct an algorithm that can provide robust person detection, wepresent a dataset with over 8 million images that was produced in a weaklysupervised manner. Through labor-intensive human annotation, the persondetection research community has produced relatively small datasets containingon the order of 100,000 images, such as the EuroCity Persons dataset, whichincludes 240,000 bounding boxes. Therefore, we have collected 8.7 millionimages of persons based on a two-step collection process, namely persondetection with an existing detector and data refinement for false positivesuppression. According to the experimental results, the Weakly SupervisedPerson Dataset (WSPD) is simple yet effective for person detectionpre-training. In the context of pre-trained person detection algorithms, ourWSPD pre-trained model has 13.38 and 6.38% better accuracy than the same modeltrained on the fully supervised ImageNet and EuroCity Persons datasets,respectively, when verified with the Caltech Pedestrian.

Weakly Supervised Dataset Collection for Robust Person Detection