Learning Local-Global Multi-Graph Descriptors for RGB-T Object Tracking
| Autor: | Chenglong Li, Chengli Zhu, Bin Luo, Xiaohao Wu, Jin Tang, Jian Zhang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Structured support vector machine
Computer science business.industry BitTorrent tracker ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Graph Visualization Search engine Minimum bounding box Video tracking 0202 electrical engineering electronic engineering information engineering Media Technology RGB color model 020201 artificial intelligence & image processing Computer vision Artificial intelligence Electrical and Electronic Engineering business |
| Zdroj: | IEEE Transactions on Circuits and Systems for Video Technology. 29:2913-2926 |
| ISSN: | 1558-2205 1051-8215 |
| DOI: | 10.1109/tcsvt.2018.2874312 |
| Popis: | RGB-thermal (RGB-T) object tracking, which has attracted much recent attention, uses thermal infrared information to assist object tracking with visible light information. However, it still faces many challenging problems, especially the background inclusion in the target bounding box which easily results in model drifting. To handle this problem, we propose a novel and general approach to learn a local-global multi-graph descriptor to suppress background effects for RGB-T tracking. Our approach relies on a novel graph learning algorithm. First, the object is represented with multiple graphs, with a set of multi-modal image patches as nodes, for the robustness to prevent deformation and partial occlusion. Second, we dynamically learn a joint graph over time with both local and global considerations using spatial smoothness and low-rank representation. In particular, we design a single unified alternating direction method of multipliers-based optimization framework to learn graph structure, edge weights, and node weights simultaneously. Third, we combine multi-graph information with corresponding graph node weights to form a robust object descriptor, and tracking is finally carried out by adopting the structured support vector machine. Extensive experiments conducted on the tracking benchmark data sets demonstrate the effectiveness of the proposed approach against the state-of-the-art RGB-T trackers. |
| Databáze: | OpenAIRE |
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