Transformer-based Action recognition in hand-object interacting scenarios

Autor:	Cho, Hoseong, Baek, Seungryul
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: 5 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2210.11387 Zobrazit plný text záznamu View this record from Arxiv