The detection of human parts (e.g., hands, face) and their correct association with individuals is an essential task, e.g., for ubiquitous human-machine interfaces and action recognition. Traditional methods often employ multi-stage processes, rely on cumbersome anchor-based systems, or do not scale well to larger part sets. This paper presents PBADet, a novel one-stage, anchor-free approach for part-body association detection. Building upon the anchor-free object representation across multi-scale feature maps, we introduce a singular part-to-body center offset that effectively encapsulates the relationship between parts and their parent bodies. Our design is inherently versatile and capable of managing multiple parts-to-body associations without compromising on detection accuracy or robustness. Comprehensive experiments on various datasets underscore the efficacy of our approach, which not only outperforms existing state-of-the-art techniques but also offers a more streamlined and efficient solution to the part-body association challenge.

翻译：人体部位（如手部、面部）的检测及其与个体的正确关联是一项关键任务，例如在人机交互和动作识别中具有广泛应用。传统方法通常采用多阶段流程，依赖繁琐的锚点系统，或者难以适应更大规模的部位集合。本文提出PBADet，一种用于部位-人体关联检测的新型单阶段无锚点方法。基于多尺度特征图上无锚点目标表示，我们引入单一的部位到人体中心偏移量，有效捕捉部位与其所属人体之间的关系。该设计具有内在通用性，能够在不影响检测精度或鲁棒性的情况下处理多部位与人体关联。在多个数据集上的全面实验验证了该方法的有效性，它不仅优于现有最先进技术，还为部位-人体关联挑战提供了更简化高效的解决方案。