论文标题
RGBD1K：用于RGB-D对象跟踪的大规模数据集和基准
RGBD1K: A Large-scale Dataset and Benchmark for RGB-D Object Tracking
论文作者
论文摘要
RGB-D对象跟踪最近引起了广泛的关注，这得益于视觉和深度通道之间的共生性能。但是，鉴于有限的带注释的RGB-D跟踪数据，大多数最先进的RGB-D跟踪器是高性能RGB的直接跟踪器的简单扩展，而无需完全利用离线训练阶段深度通道的潜在潜力。为了解决数据集缺乏问题，本文发布了一个名为RGBD1K的新RGB-D数据集。 RGBD1K总共包含1,050个序列，总计约250万帧。为了证明一般较大的RGB-D数据集的培训的好处，尤其是RGBD1K，我们开发了一个基于变压器的RGB-D跟踪器，名为SPT，是使用新数据集的将来的Visual Object对象跟踪研究的基线。使用SPT跟踪器进行广泛实验的结果表明，RGBD1K数据集的潜力提高了RGB-D跟踪的性能，从而激发了有效跟踪器设计的未来发展。数据集和代码将在项目主页上提供：https：//github.com/xuefeng-zhu5/rgbd1k。
RGB-D object tracking has attracted considerable attention recently, achieving promising performance thanks to the symbiosis between visual and depth channels. However, given a limited amount of annotated RGB-D tracking data, most state-of-the-art RGB-D trackers are simple extensions of high-performance RGB-only trackers, without fully exploiting the underlying potential of the depth channel in the offline training stage. To address the dataset deficiency issue, a new RGB-D dataset named RGBD1K is released in this paper. The RGBD1K contains 1,050 sequences with about 2.5M frames in total. To demonstrate the benefits of training on a larger RGB-D data set in general, and RGBD1K in particular, we develop a transformer-based RGB-D tracker, named SPT, as a baseline for future visual object tracking studies using the new dataset. The results, of extensive experiments using the SPT tracker emonstrate the potential of the RGBD1K dataset to improve the performance of RGB-D tracking, inspiring future developments of effective tracker designs. The dataset and codes will be available on the project homepage: https://github.com/xuefeng-zhu5/RGBD1K.
