PBADet: A One-Stage Anchor-Free Approach for Part-Body Association
Zhongpai Gao Huayi Zhou Abhishek Sharma Meng Zheng Benjamin Planche Terrence Chen Ziyan Wu
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.