文献类型：期刊文献

英文题名：A novel object tracking algorithm by fusing color and depth information based on single valued neutrosophic cross-entropy

作者：Hu, Keli[1];Ye, Jun[1];Fan, En[1];Shen, Shigen[1];Huang, Longjun[1];Pi, Jiatian[2]

机构：[1]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Zhejiang, Peoples R China;[2]Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Key Lab Wireless Sensor Network & Commun, Shanghai, Peoples R China

年份：2017

卷号：32

期号：3

起止页码：1775

外文期刊名：JOURNAL OF INTELLIGENT & FUZZY SYSTEMS

收录：SCI-EXPANDED(收录号：WOS:000395904400012)、、EI(收录号：20171003414000)、Scopus(收录号：2-s2.0-85014145561)、WOS

基金：This work was supported by National Natural Science Foundation of China under Grant no. 61603258, the Public Welfare Technology Application Research Project of Zhejiang Province in China under Grant no. 2016C31082, and the scientific research project of Shaoxing University under Grant no. 2014LG1009.

语种：英文

外文关键词：Object tracking; RGBD; fusion; single valued neutrosophic set; cross-entropy measure

外文摘要：Although appearance based trackers have been greatly improved in the last decade, they are still struggling with some challenges like occlusion, blur, fast motion, deformation, etc. As known, occlusion is still one of the soundness challenges for visual tracking. Other challenges are also not fully resolved for the existed trackers. In this work, we focus on tackling the latter problem in both color and depth domains. Neutrosophic set (NS) is as a new branch of philosophy for dealing with incomplete, indeterminate and inconsistent information. In this paper, we utilize the single valued neutrosophic set (SVNS), which is a subclass of NS, to build a robust tracker. First, the color and depth histogram are employed as the appearance features, and both features are represented in the SVNS domain via three membership functions T, I, and F. Second, the single valued neutrosophic cross-entropy measure is utilized for fusing the color and depth information. Finally, a novel SVNS based MeanShift tracker is proposed. Applied to the video sequences without serious occlusion in the Princeton RGBD Tracking dataset, the performance of our method was compared with those by the state-of-the-art trackers. The results revealed that our method outperforms these trackers when dealing with challenging factors like blur, fast motion, deformation, illumination variation, and camera jitter.