Abstract: RGBT tracking receives a surge of interest in the computer vision community,but this research field lacks a large-scale and high-diversity benchmarkdataset, which is essential for both the training of deep RGBT trackers and thecomprehensive evaluation of RGBT tracking methods. To this end, we present aLarge-scale High-diversity benchmark for RGBT tracking (LasHeR) in this work.LasHeR consists of 1224 visible and thermal infrared video pairs with more than730K frame pairs in total. Each frame pair is spatially aligned and manuallyannotated with a bounding box, making the dataset well and densely annotated.LasHeR is highly diverse capturing from a broad range of object categories,camera viewpoints, scene complexities and environmental factors across seasons,weathers, day and night. We conduct a comprehensive performance evaluation of12 RGBT tracking algorithms on the LasHeR dataset and present detailed analysisto clarify the research room in RGBT tracking. In addition, we release theunaligned version of LasHeR to attract the research interest for alignment-freeRGBT tracking, which is a more practical task in real-world applications. Thedatasets and evaluation protocols are available at:this https URL.
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