TGMCF: a tree-guided multi-modality correlation filter for visual tracking.

Liu, Qiaoyuan; Liu, Weiwei; Wang, Yuru; Ren, Jinchang; Du, Qiao; Lv, Yinghua; Sun, Haijiang

Abstract

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.

Citation

LIU, Q., LIU, W., WANG, Y., REN, J., DU, Q., LV, Y. and SUN, H. 2019. TGMCF: a tree-guided multi-modality correlation filter for visual tracking. IEEE access [online], 7, pages 166950-166963. Available from: https://doi.org/10.1109/ACCESS.2019.2943917

• Journal Article Type: Article
• Acceptance Date: Sep 19, 2019
• Online Publication Date: Sep 26, 2019
• Publication Date: Dec 31, 2019
• Deposit Date: Jul 15, 2024
• Publicly Available Date: Jul 15, 2024
• Journal: IEEE access.
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Pages: 166950-166963
• DOI: https://doi.org/10.1109/ACCESS.2019.2943917
• Keywords: Visual tracking; Tree-guided; Multimodality correlation filter
• Public URL: https://rgu-repository.worktribe.com/output/2058934