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.

本论文介绍了一种新的一阶锚点自由方法 ——PBADet，用于人体部位相关性检测，这种设计非常灵活，能够同时管理多个部位与身体的相关性，不仅在检测准确度和稳健性方面优于现有的技术，还提供了更流畅高效的解决方案。