RGB-T object tracking: Benchmark and baseline

• Published in: Pattern recognition Vol. 96; p. 106977
• Main Authors: Li, Chenglong, Liang, Xinyan, Lu, Yijuan, Zhao, Nan, Tang, Jin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2019
• Subjects: Benchmark dataset; Graph representation; Information fusion; Sparse learning; Visual tracking; Benchmark dataset; Visual tracking; Sparse learning; Graph representation; Information fusion
• DOI: 10.1016/j.patcog.2019.106977
• ISSN: 0031-3203

•A large-scale RGB-T dataset is contributed to online RGB-T object tracking. The benchmark with a dozen of baseline trackers and 5 evaluation metrics will be open to public.•A novel graph-based learning approach is proposed to learn robust RGB-T object feature representations.•A L1-optimization based sparse learning algorithm is proposed to mitigate the noises of initial weights.•Extensive experiments are conducted on the large-scale benchmark dataset, and we provide new insights and potential future research directions for RGB-T object tracking. RGB-Thermal (RGB-T) object tracking receives more and more attention due to the strongly complementary benefits of thermal information to visible data. However, RGB-T research is limited by lacking a comprehensive evaluation platform. In this paper, we propose a large-scale video benchmark dataset for RGB-T tracking. It has three major advantages over existing ones: 1) Its size is sufficiently large for large-scale performance evaluation (total number of frames: 234K, maximum number of frames per sequence: 8K). 2) The alignment between RGB-T sequence pairs is highly accurate, which does not need pre- or post-processing. 3) The occlusion levels are annotated for occlusion-sensitive performance analysis of different tracking algorithms. Moreover, we propose a novel graph-based approach to learn a robust object representation for RGB-T tracking. In particular, the tracked object is represented with a graph with image patches as nodes. Given initial weights of nodes, this graph including graph structure, node weights and edge weights is dynamically learned in a unified optimization framework. Extensive experiments on the large-scale dataset are executed to demonstrate the effectiveness of the proposed tracker against other state-of-the-art tracking methods. We also provide new insights and potential research directions to the field of RGB-T object tracking.