文献类型：期刊文献

英文题名：Real-time scale-adaptive correlation filters tracker with depth information to handle occlusion

作者：Pi, Jiatian[1];Gu, Yuzhang[1];Hu, Keli[2];Cheng, Xiaoliu[1];Zhan, Yunlong[1];Wang, Yingguan[1]

机构：[1]Shanghai Inst Microsyst & Informat Technol, 865 Changning Rd, Shanghai 200050, Peoples R China;[2]Shaoxing Univ, Comp Sci & Engn, 508 Huancheng West Rd, Shaoxing 312000, Peoples R China

年份：2016

卷号：25

期号：4

外文期刊名：JOURNAL OF ELECTRONIC IMAGING

收录：SCI-EXPANDED(收录号：WOS:000387787000035)、、EI(收录号：20163402733175)、Scopus(收录号：2-s2.0-84982245408)、WOS

基金：This work was sponsored in part by the Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) under Grant No. XDB02080005, the Special Development Fund of Shanghai Zhangjiang National Innovation Demonstration Zone under Grant No. ZJ2015-ZD-001, and the Public Welfare Technology Research Project in Zhejiang Province (2016C31082).

语种：英文

外文关键词：object tracking; correlation filter; occlusion handling; RGB-D

外文摘要：In visual object tracking, occlusions significantly undermine the performance of tracking algorithms. RGB-D cameras, such as Microsoft Kinect or the related PrimeSense camera, are widely available to consumers. Great attention has been focused on exploiting depth information for object tracking in recent years. We propose an algorithm that improves the existing correlation filter-based tracker for scale-adaptive tracking. Moreover, we utilize depth information provided by the Kinect camera to handle various types of occlusions. First, the optimal location of the target is obtained by the conventional kernelized correlation filter tracker. Then, we make use of the discriminative correlation filter for scale estimation as an independent part. At last, to further improve the tracking performance under occlusions, we present a simple yet effective occlusion handling mechanism to detect occlusion and recovery. In this mechanism, cluster analysis and object segmentation by K-means method have been applied to depth data. Numerous experiments on Princeton RGB-D tracking dataset demonstrate that the proposed algorithm outperforms several state-of-the-art trackers by successfully dealing with occlusions. (C) 2016 SPIE and IS&T