文献类型：期刊文献

英文题名：Online Visual Tracking of Weighted Multiple Instance Learning via Neutrosophic Similarity-Based Objectness Estimation

作者：Hu, Keli[1];He, Wei[2];Ye, Jun[1];Zhao, Liping[1];Peng, Hua[1,3];Pi, Jiatian[4]

机构：[1]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Peoples R China;[2]Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Key Lab Wireless Sensor Network & Commun, Shanghai 200050, Peoples R China;[3]Jishou Univ, Coll Informat Sci & Engn, Jishou 416000, Peoples R China;[4]Chongqing Normal Univ, Coll Comp & Informat Sci, Chongqing 400047, Peoples R China

年份：2019

卷号：11

期号：6

外文期刊名：SYMMETRY-BASEL

收录：SCI-EXPANDED(收录号：WOS:000475703000107)、、Scopus(收录号：2-s2.0-85069870515)、WOS

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61603258, Grant 61703280, and Grant 61662025, and in part by the Natural Science Foundation of Zhejiang Province under Grant LY19F020015.

语种：英文

外文关键词：visual tracking; neutrosophic weight; objectness; weighted multiple instance learning

外文摘要：An online neutrosophic similarity-based objectness tracking with a weighted multiple instance learning algorithm (NeutWMIL) is proposed. Each training sample is extracted surrounding the object location, and the distribution of these samples is symmetric. To provide a more robust weight for each sample in the positive bag, the asymmetry of the importance of the samples is considered. The neutrosophic similarity-based objectness estimation with object properties (super straddling) is applied. The neutrosophic theory is a new branch of philosophy for dealing with incomplete, indeterminate, and inconsistent information. By considering the surrounding information of the object, a single valued neutrosophic set (SVNS)-based segmentation parameter selection method is proposed, to produce a well-built set of superpixels which can better explain the object area at each frame. Then, the intersection and shape-distance criteria are proposed for weighting each superpixel in the SVNS domain, mainly via three membership functions, T (truth), I (indeterminacy), and F (falsity), for each criterion. After filtering out the superpixels with low response, the newly defined neutrosophic weights are utilized for weighting each sample. Furthermore, the objectness estimation information is also applied for estimating and alleviating the problem of tracking drift. Experimental results on challenging benchmark video sequences reveal the superior performance of our algorithm when confronting appearance changes and background clutters.