TGMCF: A Tree-Guided Multi-Modality Correlation Filter for Visual Tracking

doi:10.1109/access.2019.2943917

CORC > 长春光学精密机械与物理研究所 > 中国科学院长春光学精密机械与物理研究所

	TGMCF: A Tree-Guided Multi-Modality Correlation Filter for Visual Tracking
	Q.Y.Liu; W.W.Liu; Y.R.Wang; J.C.Ren; Q.Du; Y.H.Lv; H.J.Sun
刊名	Ieee Access
	2019
卷号	7 页码:166950-166963
关键词	Visual tracking,tree-guided,multimodality correlation filter,object tracking,ensemble,Computer Science,Engineering,Telecommunications
ISSN号	2169-3536
DOI	10.1109/access.2019.2943917
英文摘要	For updating the tracking models, most existing approaches have an assumption that the target changes smoothly over time. Despite their success in some cases, these approaches struggle in dealing with occlusion, illumination changes and abrupt motion which may break the temporal smoothness assumption. To tackle this problem, in this paper we propose a tree-guided visual tracking model based on the multimodality correlation filter which could estimate the target state according to the most reliable information in previous frames. We maintain a representative target state set in a tree model over the whole tracking process. Ideally, the tree model is able to capture all the landmark states of the target, and provides a confident template for the correlation filter. Therefore, we propose an optimal updating strategy to record the most recent stable and representative states for tree updating. By utilizing stable target-states for template training, the multi-modality correlation filter is able to output a more accurate target position than the baseline and the SOTA (state-of-the-art) methods. Tested on the OTB50 (object tracking benchmark) and OTB100 dataset, the proposed TGMCF has demonstrated outstanding performance on several typical tracking difficulties and overall comparative results with the SOTA trackers are obtained on several public tracking benchmarks.
语种	英语
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/63188]
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Q.Y.Liu,W.W.Liu,Y.R.Wang,et al. TGMCF: A Tree-Guided Multi-Modality Correlation Filter for Visual Tracking[J]. Ieee Access,2019,7:166950-166963.
APA	Q.Y.Liu.,W.W.Liu.,Y.R.Wang.,J.C.Ren.,Q.Du.,...&H.J.Sun.(2019).TGMCF: A Tree-Guided Multi-Modality Correlation Filter for Visual Tracking.Ieee Access,7,166950-166963.
MLA	Q.Y.Liu,et al."TGMCF: A Tree-Guided Multi-Modality Correlation Filter for Visual Tracking".Ieee Access 7(2019):166950-166963.