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.

翻译：RGB-D天体跟踪最近引起了相当多的关注,由于视觉和深度频道之间的共生关系,取得了有希望的绩效。然而,鉴于附加说明的 RGB-D 跟踪数据数量有限,大多数最先进的RGB-D 跟踪数据是高性能RGB专用跟踪器的简单扩展,而没有充分利用离线培训阶段深度频道的潜在潜力。为了解决数据集不足问题,本文件公布了名为RGB-D数据集的新的RGB-D数据集。RGBD1K 包含1 050个序列,总共约2.5M 框架。为了展示在更大的 RGB-D 数据集,特别是RGBD1K 上培训的好处,我们开发了一个基于变压器的RGB-D 跟踪器,称为小组委员会,作为未来利用新数据集进行视觉物体跟踪研究的基线。使用小组委员会跟踪器进行的广泛实验的结果显示RGBD1K 数据集的潜力,以改善RGB-D跟踪的性能,激励未来有效跟踪器的主机设计项目的发展。MGBD-DRG1 数据设置和代码将是可用的MFRG/RGRB 。