重庆大学学报2017，Vol.40Issue(7)：43-51,9.DOI:10.11835/j.issn.1000-582X.2017.07.007
基于图的流行排序的视觉跟踪
Graph-based manifold ranking for visual tracking
摘要
Abstract
This paper proposes a novel object tracking approach via graph-based manifold ranking to handle the model drift problem in the tracking-by-detection framework.The proposed approach can suppress the effects of background information caused by object deformation,scale variation and occlusion in object tracking.First,we partition the target bounding box into non-overlapping image patches,and take these image patches as graph nodes to construct k-regular graph,in which the edge weight between two neighbor nodes are measured by the distance of their low-level features.Second,we assign each patch with a weight describing the importance in representing the object,and compute it in a semi-supervised way.In particular,we initialize some patches as object patches with the weights 1,and remaining patches with the weights 0.The graph-based manifold ranking is then performed to obtain the weights of all patches.Moreover,we propose to determine the optimal scale based on multi-scale feature pyramid to address scale adaptation while improving the quality of initial patches in object tracking.Finally,we concatenate all weighted patch descriptors into a vector to represent the bounding box feature,and then integrate it into structure output (Struck) algorithm to carry out object tracking.Experimental results on several public video sequences suggest that the proposed method significantly outperforms other tracking methods.关键词
视觉跟踪/流行排序/尺度处理/Struck算法Key words
visual tracking/manifold ranking/scale adaptation/Struck algorithm分类
信息技术与安全科学引用本文复制引用
邱慧丽,宋启祥,赵楠..基于图的流行排序的视觉跟踪[J].重庆大学学报,2017,40(7):43-51,9.基金项目
国家自然科学基金资助项目(61374128,41173106,41373095) （61374128,41173106,41373095）
安徽省科技攻关计划(1501zc04048) （1501zc04048）
安徽省教育厅自然科学研究产学研重点项目(KJ2014A247) （KJ2014A247）
宿州学院智能信息处理实验室开放课题资助(2016ykf13).Supported by National Natural Science Foundation of China(61374128,41173106,41373095) （2016ykf13）
Anhui Scientific and technological project (1501ZCD4048) （1501ZCD4048）
Natual Science Industry University Research Project of Anhui Provincial Education Department (KJ2014A247) （KJ2014A247）
Openjing Foundation of Intelligent Information Processing Laborotory,Suzhou University(2016ykf13). （2016ykf13）
