Pedestrian detection under valet parking scenarios is fundamental for autonomous driving. However, the presence of pedestrians can be manifested in a variety of ways and postures under imperfect ambient conditions, which can adversely affect detection performance. Furthermore, models trained on publicdatasets that include pedestrians generally provide suboptimal outcomes for these valet parking scenarios. In this paper, wepresent the Parking Pedestrian Dataset (PPD), a large-scale fisheye dataset to support research dealing with real-world pedestrians, especially with occlusions and diverse postures. PPD consists of several distinctive types of pedestrians captured with fisheye cameras. Additionally, we present a pedestrian detection baseline on PPD dataset, and introduce two data augmentation techniques to improve the baseline by enhancing the diversity ofthe original dataset. Extensive experiments validate the effectiveness of our novel data augmentation approaches over baselinesand the dataset's exceptional generalizability.

翻译：代客泊车场景下的行人检测是实现自动驾驶的基础。然而，在非理想环境条件下，行人可能以多种姿态和方式出现，从而对检测性能产生不利影响。此外，基于包含行人的公开数据集训练的模型，在代客泊车场景中通常表现欠佳。本文提出了停车行人数据集（PPD），这是一个大规模鱼眼数据集，旨在支持针对真实行人（尤其是存在遮挡和多样化姿态的情况）的研究。PPD包含多种由鱼眼摄像头捕获的独特行人类型。此外，我们还在PPD数据集上建立了行人检测基线，并引入两种数据增强技术，通过提升原始数据集的多样性来改进该基线。大量实验验证了我们提出的新型数据增强方法相较于基线的有效性，以及该数据集卓越的泛化能力。