# Accurate Hand Keypoint Localization on Mobile Devices

## TL;DR

This paper introduces a CNN-based method for accurate 2D hand keypoint localization on mobile devices, achieving high accuracy and efficiency suitable for real-time applications.

## Contribution

A novel CNN architecture designed for efficient and accurate hand keypoint detection on mobile devices, outperforming existing methods in speed and comparable or better accuracy.

## Key findings

- Matches or exceeds state-of-the-art accuracy
- Significantly faster computational performance
- Suitable for real-time mobile applications

## Abstract

We present a novel approach for 2D hand keypoint localization from regular color input. The proposed approach relies on an appropriately designed Convolutional Neural Network (CNN) that computes a set of heatmaps, one per hand keypoint of interest. Extensive experiments with the proposed method compare it against state of the art approaches and demonstrate its accuracy and computational performance on standard, publicly available datasets. The obtained results demonstrate that the proposed method matches or outperforms the competing methods in accuracy, but clearly outperforms them in computational efficiency, making it a suitable building block for applications that require hand keypoint estimation on mobile devices.