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

IEEE Access

Haijiang Sun¹  Jinchang Ren²  Weiwei Liu³  Qiao Du³  Qiaoyuan Liu³  Yinghua Lv³  Yuru Wang³ 

[1] Chinese Academy of Sciences, Changchun Institute of Optics, Fine Mechanics and Physics, Beijing, China;Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, U.K.;Department of Information Sciences and Technology, Northeast Normal University, Changchun, China;

关键词: Visual tracking;    tree-guided;    multimodality correlation filter;   

DOI  :  10.1109/ACCESS.2019.2943917

来源: DOAJ

【 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.

【 授权许可】

Unknown