论文标题
通过扩展对象表示，身体零件的关节检测和关联
Body-Part Joint Detection and Association via Extended Object Representation
论文作者
论文摘要
自从深CNN的突破以来，对人体及其相关部位的检测（例如，面部，头或手）得到了深入的研究和大大改善。但是，这些探测器中的大多数都是独立训练的，这是将检测到的身体部位与人相关联的具有挑战性的任务。本文着重于人体及其相应部位的联合检测问题。具体而言，我们提出了一种新型的扩展对象表示，该对象表示会整合身体或其部位的中心位置偏移，并构建一个密集的单阶段锚点的身体零件零件探测器（BPJDET）。 BPJDET中的身体部分关联嵌入到包含语义和几何信息的统一表示中。因此，BPJDET匹配后不受易于发达的关联，并且具有更好的准确性速度权衡。此外，BPJDET可以无缝概括以共同检测任何身体部位。为了验证我们方法的有效性和优势，我们对城市杂物，人类和人手手机数据集进行了广泛的实验。拟议的BPJDET检测器在这三个基准测试基准上实现了最先进的关联性能，同时保持了高度的检测精度。代码在https://github.com/hhnuzhy/bpjdet中。
The detection of human body and its related parts (e.g., face, head or hands) have been intensively studied and greatly improved since the breakthrough of deep CNNs. However, most of these detectors are trained independently, making it a challenging task to associate detected body parts with people. This paper focuses on the problem of joint detection of human body and its corresponding parts. Specifically, we propose a novel extended object representation that integrates the center location offsets of body or its parts, and construct a dense single-stage anchor-based Body-Part Joint Detector (BPJDet). Body-part associations in BPJDet are embedded into the unified representation which contains both the semantic and geometric information. Therefore, BPJDet does not suffer from error-prone association post-matching, and has a better accuracy-speed trade-off. Furthermore, BPJDet can be seamlessly generalized to jointly detect any body part. To verify the effectiveness and superiority of our method, we conduct extensive experiments on the CityPersons, CrowdHuman and BodyHands datasets. The proposed BPJDet detector achieves state-of-the-art association performance on these three benchmarks while maintains high accuracy of detection. Code is in https://github.com/hnuzhy/BPJDet.
