Sciweavers

Share
ECCV
2004
Springer

Adaptive Probabilistic Visual Tracking with Incremental Subspace Update

11 years 6 months ago
Adaptive Probabilistic Visual Tracking with Incremental Subspace Update
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...
David A. Ross, Jongwoo Lim, Ming-Hsuan Yang
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2004
Where ECCV
Authors David A. Ross, Jongwoo Lim, Ming-Hsuan Yang
Comments (0)
books