LasHeR: A Large-Scale High-Diversity Benchmark for RGBT Tracking
By Chenglong Li, Wanli Xue, Yaqing Jia, Zhichen Qu, Bin Luo, Jin Tang, Dengdi Sun. IEEE Transactions on Image Processing, 2021
319 citations recorded by the source corpus.
RGBT tracking receives a surge of interest in the computer vision community, but this research field lacks a large-scale and high-diversity benchmark dataset, which is essential for both the training of deep RGBT trackers and the comprehensive evaluation of RGBT tracking methods. To this end, we present a <italic>La</italic> rge-<inline-formula> <tex-math notation="LaTeX">${s}$ </tex-math></inline-formula>cale <inline-formula> <tex-math notation="LaTeX">${H}$ </tex-math></inline-formula>igh-diversity <inline-formula> <tex-math notation="LaTeX">$\text{b}{e}$ </tex-math></inline-formula>nchmark for short-term <inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula>GBT tracking (LasHeR) in this work. LasHeR consists of 1224 visible and thermal infrared video pairs with more than 730K frame pairs in total. Each frame pair is spatially aligned and manually annotated 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 of 12 RGBT tracking algorithms on the LasHeR dataset and present detailed analysis. In addition, we release the unaligned version of LasHeR to attract the research interest for alignment-free RGBT tracking, which is a more practical task in real-world applications. The datasets and evaluation protocols are available at: <uri>https://github.com/mmic-lcl/Datasets-and-benchmark-code</uri>.
Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction
View Paper: https://www.semanticscholar.org/paper/6ef0c60a06016b277620e823ecfb219084f491ca
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Citations in the corpus
- Visible and Infrared Image Fusion Using Deep Learning (2023, methodology): “Indeed, almost all existing RGB-infrared datasets, such as LasHeR [183], RGBT234 [9] and M 3 FD [115], have some issues of misalignment.”