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ACCV
2006
Springer

Online Updating Appearance Generative Mixture Model for Meanshift Tracking

9 years 7 months ago
Online Updating Appearance Generative Mixture Model for Meanshift Tracking
This paper proposes an appearance generative mixture model based on key frames for meanshift tracking. Meanshift tracking algorithm tracks object by maximizing the similarity between the histogram in tracking window and a static histogram acquired at the beginning of tracking. The tracking therefore may fail if the appearance of the object varies substantially. Assume the key appearances of the object can be acquired before tracking, the manifold of the object appearance can be approximated by some piece-wise linear combination of these key appearances in histogram space. The generative process can be described by a bayesian graphical model. Online EM algorithm is then derived to estimate the model parameters and to update the appearance histogram. The updating histogram would improve meanshift tracking accuracy and reliability, and the model parameters infer the state of the object with respect to the key appearances. We applied this approach to track human head motion and to infer th...
Jilin Tu, Hai Tao, Thomas S. Huang
Added 13 Jun 2010
Updated 13 Jun 2010
Type Conference
Year 2006
Where ACCV
Authors Jilin Tu, Hai Tao, Thomas S. Huang
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