Two-stream convolutional neural network for accurate RGB-D fingertip detection using depth and edge information

TL;DR

This paper introduces a two-stream CNN that combines depth and edge information for precise RGB-D fingertip detection, achieving superior accuracy and real-time performance on public datasets.

Contribution

A novel two-stream CNN architecture with a slow fusion strategy that effectively integrates depth and edge data for fingertip detection.

Findings

Outperforms state-of-the-art methods on HandNet dataset

Achieves an average 3D error of 9.9mm

Provides comparable accuracy on NYU hand dataset

Abstract

Accurate detection of fingertips in depth image is critical for human-computer interaction. In this paper, we present a novel two-stream convolutional neural network (CNN) for RGB-D fingertip detection. Firstly edge image is extracted from raw depth image using random forest. Then the edge information is combined with depth information in our CNN structure. We study several fusion approaches and suggest a slow fusion strategy as a promising way of fingertip detection. As shown in our experiments, our real-time algorithm outperforms state-of-the-art fingertip detection methods on the public dataset HandNet with an average 3D error of 9.9mm, and shows comparable accuracy of fingertip estimation on NYU hand dataset.