  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.
