Pedestrian detection is a crucial field of computer vision research which can be adopted in various real-world applications (e.g., self-driving systems). However, despite noticeable evolution of pedestrian detection, pedestrian representations learned within a detection framework are usually limited to particular scene data in which they were trained. Therefore, in this paper, we propose a novel approach to construct versatile pedestrian knowledge bank containing representative pedestrian knowledge which can be applicable to various detection frameworks and adopted in diverse scenes. We extract generalized pedestrian knowledge from a large-scale pretrained model, and we curate them by quantizing most representative features and guiding them to be distinguishable from background scenes. Finally, we construct versatile pedestrian knowledge bank which is composed of such representations, and then we leverage it to complement and enhance pedestrian features within a pedestrian detection framework. Through comprehensive experiments, we validate the effectiveness of our method, demonstrating its versatility and outperforming state-of-the-art detection performances.

翻译：行人检测是计算机视觉研究中的一个关键领域，可应用于各种实际场景（如自动驾驶系统）。然而，尽管行人检测技术取得了显著进展，检测框架内学习的行人表示通常仅限于其训练所依赖的特定场景数据。因此，本文提出了一种新颖方法，通过构建包含代表性行人知识的通用行人知识库，使其适用于多种检测框架并能在不同场景中部署。我们从大规模预训练模型中提取泛化后的行人知识，并通过量化最具代表性的特征并引导其与背景场景区分来对这些知识进行精炼。最终，我们构建由这些表示组成的通用行人知识库，并将其用于补充和增强行人检测框架中的特征表示。通过全面的实验验证，我们证明了该方法的有效性，展现了其通用性，并在检测性能上超越了当前最先进的方法。