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Incremental Learning for Robust Visual Tracking (2008)
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BibTeX
@MISC{Ross08incrementallearning,
author = {David A. Ross and Jongwoo Lim and Ruei-Sung Lin and Ming-Hsuan Yang},
title = { Incremental Learning for Robust Visual Tracking},
year = {2008}
}
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Abstract
Visual tracking, in essence, deals with nonstationary image streams that change over time. While most existing algorithms are able to track objects well in controlled environments, they usually fail in the presence of significant variation of the object’s appearance or surrounding illumination. One reason for such failures is that many algorithms employ fixed appearance models of the target. Such models are trained using only appearance data available before tracking begins, which in practice limits the range of appearances that are modeled, and ignores the large volume of information (such as shape changes or specific lighting conditions) that becomes available during tracking. In this paper, we present a tracking method that incrementally learns a low-dimensional subspace representation, efficiently adapting online to changes in the appearance of the target. The model update, based on incremental algorithms for principal component analysis, includes two important features: a method for correctly updating the sample mean, and a for-
Keyphrases
robust visual tracking incremental learning tracking method appearance model incremental algorithm important feature large volume practice limit sample mean model update controlled environment specific lighting condition object appearance nonstationary image stream principal component analysis many algorithm low-dimensional subspace representation significant variation appearance data visual tracking shape change
