Skeleton-based action recognition, as a subarea of action recognition, is swiftly accumulating attention and popularity. The task is to recognize actions performed by human articulation points. Compared with other data modalities, 3D human skeleton representations have extensive unique desirable characteristics, including succinctness, robustness, racial-impartiality, and many more. We aim to provide a roadmap for new and existing researchers a on the landscapes of skeleton-based action recognition for new and existing researchers. To this end, we present a review in the form of a taxonomy on existing works of skeleton-based action recognition. We partition them into four major categories: (1) datasets; (2) extracting spatial features; (3) capturing temporal patterns; (4) improving signal quality. For each method, we provide concise yet informatively-sufficient descriptions. To promote more fair and comprehensive evaluation on existing approaches of skeleton-based action recognition, we collect ANUBIS, a large-scale human skeleton dataset. Compared with previously collected dataset, ANUBIS are advantageous in the following four aspects: (1) employing more recently released sensors; (2) containing novel back view; (3) encouraging high enthusiasm of subjects; (4) including actions of the COVID pandemic era. Using ANUBIS, we comparably benchmark performance of current skeleton-based action recognizers. At the end of this paper, we outlook future development of skeleton-based action recognition by listing several new technical problems. We believe they are valuable to solve in order to commercialize skeleton-based action recognition in the near future. The dataset of ANUBIS is available at: http://hcc-workshop.anu.edu.au/webs/anu101/home.
翻译:与其它数据模式相比,3D人类骨骼表象具有广泛独特的理想特征,包括简洁、稳健、种族中立性等。我们的目标是为新的和现有的研究人员提供一个路线图,介绍基于骨架的行动识别情况。为此,我们以分类形式对基于骨架的现有行动识别工作进行了审查。我们将其分为四大类:(1)数据集;(2)提取空间特征;(3)捕捉时间模式;(4)改进信号质量。对于每一种方法,我们提供简洁但信息丰富的描述。为了促进对现有基于骨架的行动识别方法进行更加公平和全面的评估,我们收集了基于骨架的行动识别情况,这是一个大型人类骨架数据集。与先前收集的数据集相比,ANUBIS在以下四个方面很有优势:(1) 使用最近发布的传感器;(2) 含有新的反向观点;(3) 鼓励对主题的高度热情;(4) 提高信号质量质量;(4) 在目前基于骨架的行动中,我们使用COVIA/SIMA行动,我们使用目前的核心行动。