@inproceedings{cb8981076c754ffdaf287922ef6a2178,
title = "Orderless and blurred visual tracking via spatio-temporal context",
abstract = "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.",
keywords = "Bayesian framework, Euclidean Distance, Resize, Spatio-temporal-context",
author = "Manna Dai and Peijie Lin and Lijun Wu and Zhicong Chen and Songlin Lai and Jie Zhang and Shuying Cheng and Xiangjian He",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 21st International Conference on MultiMedia Modeling, MMM 2015 ; Conference date: 05-01-2015 Through 07-01-2015",
year = "2015",
doi = "10.1007/978-3-319-14445-0_3",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "25--36",
editor = "Xiangjian He and Dacheng Tao and Hasan, {Muhammad Abul} and Suhuai Luo and Changsheng Xu and Jie Yang",
booktitle = "MultiMedia Modeling - 21st International Conference, MMM 2015, Proceedings",
address = "Germany",
}