  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.
