Title :
Basis pursuit for tracking
Author :
Wang, Roy Ruoyu ; Chen, Yunqiang ; Huang, Thomas
Author_Institution :
Beckman Inst. of Adv. Sci. & Technol., Urbana, IL, USA
fDate :
6/23/1905 12:00:00 AM
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;
Conference_Titel :
Image Processing, 2001. Proceedings. 2001 International Conference on
Conference_Location :
Thessaloniki
Print_ISBN :
0-7803-6725-1
DOI :
10.1109/ICIP.2001.959038
