Abstract 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 the head pose simultaneously in videos. Experiments verify that, our online histogram generative updating algorithm constrained by key appearance histograms avoids the drifting problem often encountered in tracking with online updating, that the enhanced meanshift algorithm is capable of tracking object of varying appearances more robustly and accurately, and that our tracking algorithm can infer the state of the object(e.g. pose) simultaneously as a bonus.
Online Updating Appearance Generative Mixture Model for Meanshift Tracking
Computer Vision – ACCV 2006 ; 7 ; 694-703
Lecture Notes in Computer Science ; 3851 , 7
2006-01-01
10 pages
Article/Chapter (Book)
Electronic Resource
English
Visual Tracking , Object Appearance , Meanshift Algorithm , Rear View , Static Histogram Computer Science , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Image Processing and Computer Vision , Artificial Intelligence (incl. Robotics) , Algorithm Analysis and Problem Complexity
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