Saliency Guided Hierarchical Robust Visual Tracking
This work addresses robust visual tracking for computer vision applications, representing an incremental improvement with a novel hybrid method.
The paper tackles robust visual tracking by proposing a saliency-guided hierarchical algorithm that handles abrupt motion and appearance variations, demonstrating superior performance over state-of-the-art methods in experiments on challenging sequences.
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.