RGB-T object tracking: Benchmark and baseline

作者：

Highlights：

• 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.

摘要

•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.

论文关键词：Visual tracking,Benchmark dataset,Sparse learning,Graph representation,Information fusion

论文评审过程：Received 2 January 2019, Revised 4 July 2019, Accepted 15 July 2019, Available online 16 July 2019, Version of Record 19 July 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.106977