Basis pursuit for tracking

Author

Wang, Roy Ruoyu ; Chen, Yunqiang ; Huang, Thomas

Author_Institution

Beckman Inst. of Adv. Sci. & Technol., Urbana, IL, USA

Volume

1

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

401

Abstract

This paper introduces a novel adaptive texture feature selection algorithm for tracking. Specifically, we provide a statistical wavelet basis paradigm to maximally separate statistical characteristics of the object-in-interest and its background. The algorithm is based upon nonlinearly selecting basis elements out of dual dictionaries in an iterative fashion to continually improve a cost function that is suitable for tracking. We demonstrate that such a selection is effective with several difficult sequences that are affected by lighting changes, occlusion and background motion

Keywords

adaptive estimation; feature extraction; image sequences; image texture; iterative methods; nonlinear estimation; statistical analysis; tracking; wavelet transforms; adaptive feature selection; background; basis elements; cost function; dual dictionaries; iterative method; nonlinear algorithm; sequences; statistical wavelet basis; texture feature selection; tracking; Cost function; Dictionaries; Iterative algorithms; Organizing; Pattern recognition; Performance evaluation; Pursuit algorithms; Stochastic processes; Vectors; Wavelet domain;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2001. Proceedings. 2001 International Conference on

Conference_Location

Thessaloniki

Print_ISBN

0-7803-6725-1

Type

conf

DOI

10.1109/ICIP.2001.959038

Filename

959038