论文标题
弱监督的数据集收集可靠的人检测
Weakly Supervised Dataset Collection for Robust Person Detection
论文作者
论文摘要
为了构建一种可以提供可靠人检测的算法，我们提供了一个数据集，其中包含超过800万张图像，这些图像以弱监督的方式生成。通过劳动密集型的人类注释，人检测研究界产生了相对较小的数据集，其中包含100,000张图像的订单，例如Eurocity Persons数据集，其中包括240,000个边界框。因此，我们根据两步收集过程收集了870万个人的图像，即具有现有检测器和数据改进的人检测，以进行假阳性抑制。根据实验结果，弱监督的人数据集（WSPD）对人检测预训练很简单，但有效。在预训练的人检测算法的背景下，我们的WSPD预训练模型的准确性比在Caltech行人的情况下进行验证时，分别在完全监督的Imagenet和Eurocity Persons数据集中训练的同一模型的精度分别高出13.38％和6.38％。
To construct an algorithm that can provide robust person detection, we present a dataset with over 8 million images that was produced in a weakly supervised manner. Through labor-intensive human annotation, the person detection research community has produced relatively small datasets containing on the order of 100,000 images, such as the EuroCity Persons dataset, which includes 240,000 bounding boxes. Therefore, we have collected 8.7 million images of persons based on a two-step collection process, namely person detection with an existing detector and data refinement for false positive suppression. According to the experimental results, the Weakly Supervised Person Dataset (WSPD) is simple yet effective for person detection pre-training. In the context of pre-trained person detection algorithms, our WSPD pre-trained model has 13.38 and 6.38% better accuracy than the same model trained on the fully supervised ImageNet and EuroCity Persons datasets, respectively, when verified with the Caltech Pedestrian.
