Adaptive Probabilistic Visual Tracking with Incremental Subspace Update

David Ross, Jongwoo Lim, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingChapter

87 Scopus citations

Abstract

Visual tracking, in essence, deals with non-stationary data streams that change over time. While most existing algorithms are able to track objects well in controlled environments, they usually fail if there is a significant change in object appearance or surrounding illumination. The reason being that these visual tracking algorithms operate on the premise that the models of the objects being tracked are invariant to internal appearance change or external variation such as lighting or viewpoint. Consequently most tracking algorithms do not update the models once they are built or learned at the outset. In this paper, we present an adaptive probabilistic tracking algorithm that updates the models using an incremental update of eigenbasis. To track objects in two views, we use an effective probabilistic method for sampling affine motion parameters with priors and predicting its location with a maximum a posteriori estimate. Borne out by experiments, we demonstrate the proposed method is able to track objects well under large lighting, pose and scale variation with close to real-time performance.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsTomáš Pajdla, Jiří Matas
PublisherSpringer Verlag
Pages470-482
Number of pages13
ISBN (Print)3540219838, 9783540219835
DOIs
StatePublished - 2004 Jan 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3022
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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  • Cite this

    Ross, D., Lim, J., & Yang, M. H. (2004). Adaptive Probabilistic Visual Tracking with Incremental Subspace Update. In T. Pajdla, & J. Matas (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 470-482). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3022). Springer Verlag. https://doi.org/10.1007/978-3-540-24671-8_37