@inproceedings{5b99ab3c53d7412da9e9de44c41ce288,
title = "Self-tuning motion model for visual tracking",
abstract = "In visual tracking, how to select a suitable motion model is an important problem to deal with, since the movements in real world are always irregular in most cases. We propose a self-tuning motion model for target tracking in this paper, where the current motion model is computed according to the relative distance of the target positions in the last two frames. Our method has achieved excellent performance when experimenting on the sequences where the targets move unstably, abruptly or even when partial occlusion exists, and the method is particularly robust to the unsuitable initial motion model.",
keywords = "Logistic regression, Self-tuning motion models, Visual tracking",
author = "Hangkai Tan and Qingjie Zhao and Xiongpeng Wang",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2017.; 3rd International Conference on Cognitive Systems and Information Processing, ICCSIP 2016 ; Conference date: 19-11-2016 Through 23-11-2016",
year = "2017",
doi = "10.1007/978-981-10-5230-9\_8",
language = "English",
isbn = "9789811052293",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "74--81",
editor = "Fuchun Sun and Huaping Liu and Dewen Hu",
booktitle = "Cognitive Systems and Signal Processing - 3rd International Conference, ICCSIP 2016, Revised Selected Papers",
address = "Germany",
}