Robust detection of vulnerable road users is a safety critical requirement for the deployment of autonomous vehicles in heterogeneous traffic. One of the most complex outstanding challenges is that of partial occlusion where a target object is only partially available to the sensor due to obstruction by another foreground object. A number of leading pedestrian detection benchmarks provide annotation for partial occlusion, however each benchmark varies greatly in their definition of the occurrence and severity of occlusion. Recent research demonstrates that a high degree of subjectivity is used to classify occlusion level in these cases and occlusion is typically categorized into 2 to 3 broad categories such as partially and heavily occluded. This can lead to inaccurate or inconsistent reporting of pedestrian detection model performance depending on which benchmark is used. This research introduces a novel, objective benchmark for partially occluded pedestrian detection to facilitate the objective characterization of pedestrian detection models. Characterization is carried out on seven popular pedestrian detection models for a range of occlusion levels from 0-99%, in order to demonstrate the efficacy and increased analysis capabilities of the proposed characterization method. Results demonstrate that pedestrian detection performance degrades, and the number of false negative detections increase as pedestrian occlusion level increases. Of the seven popular pedestrian detection routines characterized, CenterNet has the greatest overall performance, followed by SSDlite. RetinaNet has the lowest overall detection performance across the range of occlusion levels.

翻译：弱势道路使用者的鲁棒检测是自动驾驶车辆在混合交通中部署的安全关键要求。其中最复杂的持续挑战之一是部分遮挡问题，即目标物体因被其他前景物体遮挡而仅部分暴露于传感器。多个领先的行人检测基准数据集提供了部分遮挡的标注，但每个基准在遮挡发生及严重程度的定义上存在显著差异。近期研究表明，在这些案例中，遮挡级别的分类存在高度主观性，且遮挡通常被划分为2至3个宽泛类别，例如部分遮挡和严重遮挡。这可能导致行人检测模型性能报告的不准确或不一致，具体取决于所使用的基准数据集。本研究引入了一种新颖、客观的部分遮挡行人检测基准，以促进行人检测模型的客观特征表征。针对七种流行的行人检测模型，在0-99%的遮挡级别范围内进行了特征表征，以证明所提出的特征表征方法的有效性和增强的分析能力。结果表明，随着行人遮挡级别的增加，行人检测性能下降，假阴性检测数量增加。在七种流行的行人检测方法中，CenterNet的性能整体最优，其次是SSDlite，而RetinaNet在遮挡级别范围内的整体检测性能最低。