Object Tracking: Feature Selection and Confidence Propagation

CC
CRV
2004
1st Canadian Conference on Computer and Robot Vision (CRV'04)
Abstract - Object Tracking: Feature Selection and Confidence Propagation

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1st Canadian Conference on Computer and Robot Vision (CRV'04)

University of Western Ontario, London, Ontario, Canada

May 17-May 19

ISBN: 0-7695-2127-4

	ASCII Text	x
	Juhua Zhu, Stuart C. Schwartz, Bede Liu, "Object Tracking: Feature Selection and Confidence Propagation," Computer and Robot Vision, Canadian Conference, pp. 18-21, 1st Canadian Conference on Computer and Robot Vision (CRV'04), 2004.

	BibTex	x
	@article{ 10.1109/CCCRV.2004.1301416, author = {Juhua Zhu and Stuart C. Schwartz and Bede Liu}, title = {Object Tracking: Feature Selection and Confidence Propagation}, journal ={Computer and Robot Vision, Canadian Conference}, volume = {0}, year = {2004}, isbn = {0-7695-2127-4}, pages = {18-21}, doi = {http://doi.ieeecomputersociety.org/10.1109/CCCRV.2004.1301416}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Computer and Robot Vision, Canadian Conference TI - Object Tracking: Feature Selection and Confidence Propagation SN - 0-7695-2127-4 SP18 EP21 A1 - Juhua Zhu, A1 - Stuart C. Schwartz, A1 - Bede Liu, PY - 2004 KW - null VL - 0 JA - Computer and Robot Vision, Canadian Conference ER -

Juhua Zhu, Princeton University

Stuart C. Schwartz, Princeton University

Bede Liu, Princeton University

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CCCRV.2004.1301416

Choosing unique and invariant features is the first important step in object tracking. In this paper, we present a method to find proper-sized and irregularly-shaped trackable features, the use of which can outperform procedures using normal square features. The notion of confidence associated with each feature is introduced as the feature propagates. The use of confidence results in robust tracking even when occlusion is present. Based on the translational displacement of each feature, the affine motion of the object can be accurately estimated. This approach has been tested on a wide variety of video sequences and produces good tracking results.

Citation:

Juhua Zhu, Stuart C. Schwartz, Bede Liu, "Object Tracking: Feature Selection and Confidence Propagation," crv, pp.18-21, 1st Canadian Conference on Computer and Robot Vision (CRV'04), 2004

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