提出新方法客观量化自行车被遮挡程度，提升自动驾驶安全检测
Objective Bicycle Occlusion Level Classification using a Deformable Parts-Based Model
- 基于部件的检测模型，分解自行车为可识别的语义部分
- 首次实现对自行车遮挡等级的客观量化，超越传统主观评估
- 适合自动驾驶中行人/骑行者检测算法的性能评估与改进
道路安全是重大挑战，尤其对最易受伤害的骑行者而言。本研究通过先进计算机视觉技术，提出一种新型自行车遮挡等级分类基准。利用基于部件的检测模型，对图像进行标注并处理于定制检测流程中。提出一种新方法，客观量化自行车语义部件的可见性与遮挡水平。结果表明，该模型能稳健量化自行车的可见性与遮挡程度，显著优于当前主流的主观评估方法。该方法的广泛应用将有助于准确报告自动驾驶系统对被遮挡骑行者的检测性能，推动更鲁棒的弱势道路使用者检测技术发展。
原文摘要 · Abstract (English)
Road safety is a critical challenge, particularly for cyclists, who are among the most vulnerable road users. This study aims to enhance road safety by proposing a novel benchmark for bicycle occlusion level classification using advanced computer vision techniques. Utilizing a parts-based detection model, images are annotated and processed through a custom image detection pipeline. A novel method of bicycle occlusion level is proposed to objectively quantify the visibility and occlusion level of bicycle semantic parts. The findings indicate that the model robustly quantifies the visibility and occlusion level of bicycles, a significant improvement over the subjective methods used by the current state of the art. Widespread use of the proposed methodology will facilitate accurate performance reporting of cyclist detection algorithms for occluded cyclists, informing the development of more robust vulnerable road user detection methods for autonomous vehicles.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。