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This paper presents a new approach to using locally linear embedding (LLE) method in object tracking problems. By means of measuring the divergence of the K nearest neighbors of test data, a novel method is proposed to distinguish object from background directly through the LLE embedding results. Avoiding training a mapping function, this approach is less dependent on a beforehand training set of object compare to other attempts of utilizing manifold embedding method on object tracking. Besides, an asymmetric version of LLE is derived to improve the tracking performance. A Bayesian inference framework is built to apply this approach to visual tracking problem using particle filter. Experimental results demonstrate both efficiency and adaptability of our algorithm.
Image and Signal Processing (CISP), 2010 3rd International Congress on (Volume:1 )
Date of Conference: 16-18 Oct. 2010