A 3D hand pose estimation architecture based on depth camera

Zhaolong Deng; Yanliang Qiu; Xintao Xie; Zuanhui Lin

doi:10.1117/12.2671350

16 March 2023 A 3D hand pose estimation architecture based on depth camera

Zhaolong Deng, Yanliang Qiu, Xintao Xie, Zuanhui Lin

Proceedings Volume 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022); 125930Z (2023) https://doi.org/10.1117/12.2671350
Event: 2nd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 2022, Guangzhou, China

Abstract

Considering the problem of the inability to obtain accurate depth information in 3D pose estimation, this research attempts to use a depth camera to obtain accurate depth information to solve this problem and achieve good results. In the process of research, it is found that the general object detection and evaluation method is not accurate enough under the framework proposed in this paper, so this research proposes an evaluation method suitable for this framework. A standardizer is also designed to optimize the detection effect while achieving efficient tracking objects. Ultimately, inference time is reduced by 35%. The implementation of this research architecture is open-sourced at https://github.com/DumbZarro/BuddHand.

Citation Download Citation

Zhaolong Deng, Yanliang Qiu, Xintao Xie, and Zuanhui Lin "A 3D hand pose estimation architecture based on depth camera", Proc. SPIE 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 125930Z (16 March 2023); https://doi.org/10.1117/12.2671350

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