3D hand tracking in a stochastic approximation setting

Desmond Chik; Jochen Trumpf; Nicol N. Schraudolph

doi:10.1007/978-3-540-75703-0_10

3D hand tracking in a stochastic approximation setting

Desmond Chik^*, Jochen Trumpf, Nicol N. Schraudolph

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

This paper introduces a hand tracking system with a theoretical proof of convergence. The tracking system follows a model-based approach and uses image-based cues, namely silhouettes and colour constancy. We show that, with the exception of a small set of parameter configurations, the cost function of our tracker has a well-behaved unique minimum. The convergence proof for the tracker relies on the convergence theory in stochastic approximation. We demonstrate that our tracker meets the sufficient conditions for stochastic approximation to hold locally. Experimental results on synthetic images generated from real hand motions show the feasibility of this approach.

Original language	English
Title of host publication	Human Motion - Understanding, Modeling, Capture and Animation - Second Workshop, Human Motion 2007, Proceedings
Publisher	Springer Verlag
Pages	136-151
Number of pages	16
ISBN (Print)	9783540757023
DOIs	https://doi.org/10.1007/978-3-540-75703-0_10
Publication status	Published - 2007
Event	2nd Workshop on Human Motion Understanding, Modeling, Capture and Animation - Rio de Janeiro, Brazil Duration: 20 Oct 2007 → 20 Oct 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4814 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd Workshop on Human Motion Understanding, Modeling, Capture and Animation
Country/Territory	Brazil
City	Rio de Janeiro
Period	20/10/07 → 20/10/07

Access to Document

10.1007/978-3-540-75703-0_10

Cite this

Chik, D., Trumpf, J., & Schraudolph, N. N. (2007). 3D hand tracking in a stochastic approximation setting. In Human Motion - Understanding, Modeling, Capture and Animation - Second Workshop, Human Motion 2007, Proceedings (pp. 136-151). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4814 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-540-75703-0_10

Chik, Desmond ; Trumpf, Jochen ; Schraudolph, Nicol N. / 3D hand tracking in a stochastic approximation setting. Human Motion - Understanding, Modeling, Capture and Animation - Second Workshop, Human Motion 2007, Proceedings. Springer Verlag, 2007. pp. 136-151 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "3D hand tracking in a stochastic approximation setting",

abstract = "This paper introduces a hand tracking system with a theoretical proof of convergence. The tracking system follows a model-based approach and uses image-based cues, namely silhouettes and colour constancy. We show that, with the exception of a small set of parameter configurations, the cost function of our tracker has a well-behaved unique minimum. The convergence proof for the tracker relies on the convergence theory in stochastic approximation. We demonstrate that our tracker meets the sufficient conditions for stochastic approximation to hold locally. Experimental results on synthetic images generated from real hand motions show the feasibility of this approach.",

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Chik, D, Trumpf, J & Schraudolph, NN 2007, 3D hand tracking in a stochastic approximation setting. in Human Motion - Understanding, Modeling, Capture and Animation - Second Workshop, Human Motion 2007, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4814 LNCS, Springer Verlag, pp. 136-151, 2nd Workshop on Human Motion Understanding, Modeling, Capture and Animation, Rio de Janeiro, Brazil, 20/10/07. https://doi.org/10.1007/978-3-540-75703-0_10

3D hand tracking in a stochastic approximation setting. / Chik, Desmond; Trumpf, Jochen; Schraudolph, Nicol N.
Human Motion - Understanding, Modeling, Capture and Animation - Second Workshop, Human Motion 2007, Proceedings. Springer Verlag, 2007. p. 136-151 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4814 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Chik, Desmond

AU - Trumpf, Jochen

AU - Schraudolph, Nicol N.

PY - 2007

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N2 - This paper introduces a hand tracking system with a theoretical proof of convergence. The tracking system follows a model-based approach and uses image-based cues, namely silhouettes and colour constancy. We show that, with the exception of a small set of parameter configurations, the cost function of our tracker has a well-behaved unique minimum. The convergence proof for the tracker relies on the convergence theory in stochastic approximation. We demonstrate that our tracker meets the sufficient conditions for stochastic approximation to hold locally. Experimental results on synthetic images generated from real hand motions show the feasibility of this approach.

AB - This paper introduces a hand tracking system with a theoretical proof of convergence. The tracking system follows a model-based approach and uses image-based cues, namely silhouettes and colour constancy. We show that, with the exception of a small set of parameter configurations, the cost function of our tracker has a well-behaved unique minimum. The convergence proof for the tracker relies on the convergence theory in stochastic approximation. We demonstrate that our tracker meets the sufficient conditions for stochastic approximation to hold locally. Experimental results on synthetic images generated from real hand motions show the feasibility of this approach.

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DO - 10.1007/978-3-540-75703-0_10

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SN - 9783540757023

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Human Motion - Understanding, Modeling, Capture and Animation - Second Workshop, Human Motion 2007, Proceedings

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Chik D, Trumpf J, Schraudolph NN. 3D hand tracking in a stochastic approximation setting. In Human Motion - Understanding, Modeling, Capture and Animation - Second Workshop, Human Motion 2007, Proceedings. Springer Verlag. 2007. p. 136-151. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-75703-0_10

3D hand tracking in a stochastic approximation setting

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