Online Boosting is an effective incremental learning method which can update weak classifiers efficiently according to the object being trackedt. It is a promising technique for o...
Sparse representation has been applied to visual tracking by finding the best candidate with minimal reconstruction error using target templates. However most sparse representati...
We present a probabilistic multi-cue tracking approach constructed by employing a novel randomized template tracker and a constant color model based particle filter. Our approach ...
Most existing tracking algorithms construct a representation of a target object prior to the tracking task starts, and utilize invariant features to handle appearance variation of...
Jongwoo Lim, David A. Ross, Ruei-Sung Lin, Ming-Hs...
We propose a new method addressing the problem of template drift, a common phenomenon in which the target gradually shifts away from the template in object tracking. Much effort h...