Fingertip in the Eye: A cascaded CNN pipeline for the real-time fingertip detection in egocentric videos

Abstract

We introduce a new pipeline for hand localization and fingertip detection. For RGB images captured from an egocentric vision mobile camera, hand and fingertip detection remains a challenging problem due to factors like background complexity and hand shape variety. To address these issues accurately and robustly, we build a large scale dataset named Ego-Fingertip and propose a bi-level cascaded pipeline of convolutional neural networks, namely, Attention-based Hand Detector as well as Multi-point Fingertip Detector. The proposed method significantly tackles challenges and achieves satisfactorily accurate prediction and real-time performance compared to previous hand and fingertip detection methods.

5 pages, 8 figures

References in corpus (1)

• Caffe: Convolutional Architecture for Fast Feature Embedding

Cited by in corpus (3)

• Unified Learning Approach for Egocentric Hand Gesture Recognition and Fingertip Detection
• Two-stream convolutional neural network for accurate RGB-D fingertip detection using depth and edge information
• Anchors Based Method for Fingertips Position Estimation from a Monocular RGB Image using Deep Neural Network