PPD：一个用于自动驾驶的代客泊车鱼眼行人数据集
PPD: A New Valet Parking Pedestrian Fisheye Dataset for Autonomous Driving
AI总结：
本文提出大规模代客泊车鱼眼行人数据集PPD，并引入两种数据增强方法改进检测基线，实验验证了方法有效性和数据集泛化能力。
摘要：
代客泊车场景下的行人检测是自动驾驶的基础任务。然而，在不理想的环境条件下，行人可能以多种方式和姿态出现，这会对检测性能产生不利影响。此外，在包含行人的公开数据集上训练的模型通常在这些代客泊车场景中只能得到次优结果。本文提出了Parking Pedestrian Dataset（PPD），一个大规模鱼眼数据集，用于支持面向真实世界行人的研究，尤其是存在遮挡和多样化姿态的情况。PPD包含由鱼眼相机采集的多种不同类型的行人。此外，我们给出了PPD数据集上的行人检测基线，并引入两种数据增强技术，通过增强原始数据集的多样性来改进基线。大量实验验证了我们提出的新型数据增强方法相对于基线的有效性，以及该数据集出色的泛化能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
