Abstract.In this paper,a novel and robust method which exploits the spatio-temporal context for orderless and blurred visual tracking is presented.This lets the tracker adapt to both rigid and deformable objects on-line even if the image is blurred.We observe that a RGB vectorof animage which is resizedinto a small fixed size can keep enough useful information.Based on this observation and computational reasons,we propose to resize the windows ofboth template and candidate target images into 2 × 2 and use Euclidean Distance to compute the similarity between these two RGB imagevectors for the preliminary screening.We then apply spatio-temporal context based on Bayesian framework to further compute a confidence map for obtaining the best target location.Experimental results on challenging video sequences in MATLAB without code optimization show the proposed tracking method outperforms eightstate-of-the-art methods.

Visual tracking can be particularly interpreted as a process of searching for targets and optimizing the searching. In this paper, we present a novel tracker framework for tracking shaking targets. We formulate the underlying geometrical relevance between a search scope and a target displacement. A uniform sampling among the search scopes is implemented by sliding windows. To alleviate any possible redundant matching, we propose a double-template structure comprising of initial and previous tracking results. The element-wise similarities between a template and its candidates are calculated by jointly using kernel functions which provide a better outlier rejection property. The STC algorithm is used to improve the tracking results by maximizing a confidence map incorporating temporal and spatial context cues about the tracked targets. For better adaptation to appearance variations, we employ a linear interpolation to update the context prior probability of the STC method. Both qualitative and quantitative evaluations are performed on all sequences that contain shaking motions and are selected

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