  A saliency guided hierarchical visual tracking (SHT) algorithm containing
global and local search phases is proposed in this paper. In global search, a
top-down saliency model is novelly developed to handle abrupt motion and
appearance variation problems. Nineteen feature maps are extracted first and
combined with online learnt weights to produce the final saliency map and
estimated target locations. After the evaluation of integration mechanism, the
optimum candidate patch is passed to the local search. In local search, a
superpixel based HSV histogram matching is performed jointly with an L2-RLS
tracker to take both color distribution and holistic appearance feature of the
object into consideration. Furthermore, a linear refinement search process with
fast iterative solver is implemented to attenuate the possible negative
influence of dominant particles. Both qualitative and quantitative experiments
are conducted on a series of challenging image sequences. The superior
performance of the proposed method over other state-of-the-art algorithms is
demonstrated by comparative study.
