Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline
This addresses the data bottleneck for RGB-T tracking in UAV applications, enabling more robust tracking in diverse scenarios, though it is incremental in providing a dataset and baseline.
The paper tackles the lack of paired training data for visible-thermal (RGB-T) object tracking by constructing a large-scale benchmark with 500 sequences and 1.7 million high-resolution frame pairs, and introduces a new baseline method that achieves robust performance through hierarchical fusion.
With the popularity of multi-modal sensors, visible-thermal (RGB-T) object tracking is to achieve robust performance and wider application scenarios with the guidance of objects' temperature information. However, the lack of paired training samples is the main bottleneck for unlocking the power of RGB-T tracking. Since it is laborious to collect high-quality RGB-T sequences, recent benchmarks only provide test sequences. In this paper, we construct a large-scale benchmark with high diversity for visible-thermal UAV tracking (VTUAV), including 500 sequences with 1.7 million high-resolution (1920 $\times$ 1080 pixels) frame pairs. In addition, comprehensive applications (short-term tracking, long-term tracking and segmentation mask prediction) with diverse categories and scenes are considered for exhaustive evaluation. Moreover, we provide a coarse-to-fine attribute annotation, where frame-level attributes are provided to exploit the potential of challenge-specific trackers. In addition, we design a new RGB-T baseline, named Hierarchical Multi-modal Fusion Tracker (HMFT), which fuses RGB-T data in various levels. Numerous experiments on several datasets are conducted to reveal the effectiveness of HMFT and the complement of different fusion types. The project is available at here.