A unified framework for 3D hand tracking

This data was imported from DBLP:

Authors: Poudel, R.P.K., Fonseca, J.A.S., Zhang, J.J. and Nait-Charif, H.

Editors: Bebis, G. et al.

https://doi.org/10.1007/978-3-642-41914-0

Journal: ISVC (1)

Volume: 8033

Pages: 129-139

Publisher: Springer

This source preferred by Hammadi Nait-Charif, Jose Fonseca and Jian Jun Zhang

This data was imported from Scopus:

Authors: Poudel, R.P.K., Fonseca, J.A.S., Zhang, J.J. and Nait-Charif, H.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 8033 LNCS

Issue: PART 1

Pages: 129-139

eISSN: 1611-3349

ISSN: 0302-9743

DOI: 10.1007/978-3-642-41914-0_14

Discriminative techniques are good for hand part detection, however they fail due to sensor noise and high inter-finger occlusion. Additionally, these techniques do not incorporate any kinematic or temporal constraints. Even though model-based descriptive (for example Markov Random Field) or generative (for example Hidden Markov Model) techniques utilize kinematic and temporal constraints well, they are computationally expensive and hardly recover from tracking failure. This paper presents a unified framework for 3D hand tracking, utilizing the best of both methodologies. Hand joints are detected using a regression forest, which uses an efficient voting technique for joint location prediction. The voting distributions are multimodal in nature; hence, rather than using the highest scoring mode of the voting distribution for each joint separately, we fit the five high scoring modes of each joint on a tree-structure Markovian model along with kinematic prior and temporal information. Experimentally, we observed that relying on discriminative technique (i.e. joints detection) produces better results. We therefore efficiently incorporate this observation in our framework by conditioning 50% low scoring joints modes with remaining high scoring joints mode. This strategy reduces the computational cost and produces good results for 3D hand tracking on RGB-D data. © 2013 Springer-Verlag.

The data on this page was last updated at 05:24 on October 27, 2020.