Comparison of real-time CNN-based methods for finger-level hand segmentation

Gibran Benitez-Garcia; Natsuki Takayama; Jesus Olivares-Mercado; Gabriel Sanchez-Perez; Hiroki Takahashi

doi:10.1117/12.2626091

30 April 2022 Comparison of real-time CNN-based methods for finger-level hand segmentation

Gibran Benitez-Garcia, Natsuki Takayama, Jesus Olivares-Mercado, Gabriel Sanchez-Perez, Hiroki Takahashi

Author Affiliations +

Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 121770P (2022) https://doi.org/10.1117/12.2626091
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China

Abstract

Hand segmentation is usually considered a pixel-wise binary classification problem, where the foreground hand is meant to be recognized in an input image. However, we envision that finger-level hand segmentation is more useful for applications like hand gesture and sign language recognition. Therefore, in this paper, we compare five state-of-the-art (SOTA) real-time semantic segmentation methods for the task of finger-level hand segmentation. To do that, we introduce two subsets consisted of 1,000 images manually annotated pixel-wise selected from new proposed datasets of hand gesture and world-level sign language recognition. With these subsets, we evaluate the accuracy of the recent SOTA methods of DABNet, FastSCNN, FC-HardNet, FASSDNet, and DDRNet. Since each subset has relatively few images (500), we introduce a simple yet effective loss function to train with synthetic data that includes the same annotations. Finally, we present a real-time performance evaluation of the five algorithms on the NVIDIA Jetson family of GPU-powered embedded systems, including Jetson Xavier NX, Jetson TX2, and Jetson Nano.

Citation Download Citation

Gibran Benitez-Garcia, Natsuki Takayama, Jesus Olivares-Mercado, Gabriel Sanchez-Perez, and Hiroki Takahashi "Comparison of real-time CNN-based methods for finger-level hand segmentation", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 121770P (30 April 2022); https://doi.org/10.1117/12.2626091

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