Orderless and blurred visual tracking via spatio-temporal context

Publication Type:
Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, 8935 pp. 25 - 36
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© Springer International Publishing Switzerland 2015. In this paper, a novel and robust method which exploits the spatiotemporal 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 vector of an image which is resized into a small fixed size can keep enough useful information. Based on this observation and computational reasons, we propose to resize the windows of both template and candidate target images into 2×2 and use Euclidean Distance to compute the similarity between these two RGB image vectors 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 eight state-of-the-art methods.
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