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Sensors 2014, 14(7), 12023-12058;

Robust Arm and Hand Tracking by Unsupervised Context Learning

Cosys lab, Antwerp University, Paardenmarkt 92, 2000 Antwerp, Belgium
Image Processing and Interpretation, iMinds, Ghent University, St-Pietersnieuwstraat 41, 9000 Ghent, Belgium
Author to whom correspondence should be addressed.
Received: 4 April 2014 / Revised: 29 June 2014 / Accepted: 1 July 2014 / Published: 7 July 2014
(This article belongs to the Special Issue HCI In Smart Environments)
Full-Text   |   PDF [14175 KB, uploaded 7 July 2014]


Hand tracking in video is an increasingly popular research field due to the rise of novel human-computer interaction methods. However, robust and real-time hand tracking in unconstrained environments remains a challenging task due to the high number of degrees of freedom and the non-rigid character of the human hand. In this paper, we propose an unsupervised method to automatically learn the context in which a hand is embedded. This context includes the arm and any other object that coherently moves along with the hand. We introduce two novel methods to incorporate this context information into a probabilistic tracking framework, and introduce a simple yet effective solution to estimate the position of the arm. Finally, we show that our method greatly increases robustness against occlusion and cluttered background, without degrading tracking performance if no contextual information is available. The proposed real-time algorithm is shown to outperform the current state-of-the-art by evaluating it on three publicly available video datasets. Furthermore, a novel dataset is created and made publicly available for the research community. View Full-Text
Keywords: hand tracking; particle filter; unsupervised learning; random forest; context learning; importance sampling hand tracking; particle filter; unsupervised learning; random forest; context learning; importance sampling
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Spruyt, V.; Ledda, A.; Philips, W. Robust Arm and Hand Tracking by Unsupervised Context Learning. Sensors 2014, 14, 12023-12058.

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