Human Pose Estimation (HPE) is one of the fundamental problems in computer vision. It has applications ranging from virtual reality, human behavior analysis, video surveillance, anomaly detection, self-driving to medical assistance. The main objective of HPE is to obtain the person's posture from the given input. Among different paradigms for HPE, one paradigm is called bottom-up multi-person pose estimation. In the bottom-up approach, initially, all the key points of the targets are detected, and later in the optimization stage, the detected key points are associated with the corresponding targets. This review paper discussed the recent advancements in bottom-up approaches for the HPE and listed the possible high-quality datasets used to train the models. Additionally, a discussion of the prominent bottom-up approaches and their quantitative results on the standard performance matrices are given. Finally, the limitations of the existing methods are highlighted, and guidelines of the future research directions are given.
翻译:人类粒子估计(HPE)是计算机视觉的根本问题之一,其应用范围从虚拟现实、人类行为分析、视频监视、异常现象检测、自我驾驶到医疗援助,其主要目标是从给定的投入中获取个人姿势,在人类粒子估计的不同范式中,一个范式称为自下而上多人构成估计,在自下而上的方法中,最初发现所有目标的要点,后来在优化阶段,发现的关键点与相应的目标相关联,本审查文件讨论了自下而上的方法的最新进展,并列出了用于培训模型的可能高质量数据集,此外,还介绍了突出的自下而上方法及其在标准性能矩阵上的量化结果,最后强调了现有方法的局限性,并提出了未来研究方向的指导方针。