Fingertip in the Eye: A cascaded CNN pipeline for the real-time fingertip detection in egocentric videos
This work addresses a domain-specific problem in computer vision for applications like human-computer interaction, but it appears incremental as it builds on existing detection methods.
The paper tackles the challenging problem of hand and fingertip detection in egocentric RGB videos by proposing a bi-level cascaded CNN pipeline, which achieves accurate prediction and real-time performance compared to previous methods.
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.