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2016 | OriginalPaper | Buchkapitel

Fingertip in the Eye: An Attention-Based Method for Real-Time Hand Tracking and Fingertip Detection in Egocentric Videos

verfasst von : Xiaorui Liu, Yichao Huang, Xin Zhang, Lianwen Jin

Erschienen in: Pattern Recognition

Verlag: Springer Singapore

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Abstract

The hand and fingertip tracking is the crucial part in the egocentric vision interaction, and it remains a challenging problem due to various factors like dynamic environment and hand deformation. We propose a convolutional neural network (CNN) based method for the real-time and accurate hand tracking and fingertip detection in RGB sequences captured by an egocentric mobile camera. Firstly, we build a large scale dataset, Ego-Finger, containing plenty of scenarios and human labeled ground truth. Secondly, we propose a two stage CNN pipeline, i.e., the human vision inspired Attention-based Hand Tracker (AHT) and the hand physical constrained Multi-Points Fingertip Detector (MFD). Comparing with state-of-the-art methods, the proposed method achieves very promising results in the real-time fashion.

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Vorheriges Kapitel Road Extraction Based on Direction Consistency Segmentation

Nächstes Kapitel Multiple-Classifiers Based Hand Gesture Recognition

Ego-Finger dataset is available at:

http://www.hcii-lab.net/data/SCUTEgoFinger/index.htm.

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Titel: Fingertip in the Eye: An Attention-Based Method for Real-Time Hand Tracking and Fingertip Detection in Egocentric Videos
verfasst von: Xiaorui Liu
Yichao Huang
Xin Zhang
Lianwen Jin
Verlag: Springer Singapore
Buch: Pattern Recognition
Print ISBN: 978-981-10-3001-7

Electronic ISBN: 978-981-10-3002-4

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-981-10-3002-4_12

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