论文标题
手动相互作用方案中基于变压器的动作识别
Transformer-based Action recognition in hand-object interacting scenarios
论文作者
论文摘要
该报告描述了Egentric和Multi-View摄像机挑战的ECCV 2022人体，手和活动（HBHA）的第二名解决方案：行动识别。这项挑战旨在在以自我为中心的视图中识别手动对象的相互作用。我们提出了一个框架，该框架估算两只手的关键点和一个具有基于变压器的关键点估算器的对象，并根据估计关键点识别操作。我们在测试集上获得了87.19％的前1个精度。
This report describes the 2nd place solution to the ECCV 2022 Human Body, Hands, and Activities (HBHA) from Egocentric and Multi-view Cameras Challenge: Action Recognition. This challenge aims to recognize hand-object interaction in an egocentric view. We propose a framework that estimates keypoints of two hands and an object with a Transformer-based keypoint estimator and recognizes actions based on the estimated keypoints. We achieved a top-1 accuracy of 87.19% on the testset.
