RGBD (RGB plus depth) object tracking is gaining momentum as RGBD sensors
have become popular in many application fields such as robotics.However, the
best RGBD trackers are extensions of the state-of-the-art deep RGB trackers.
They are trained with RGB data and the depth channel is used as a sidekick for
subtleties such as occlusion detection. This can be explained by the fact that
there are no sufficiently large RGBD datasets to 1) train deep depth trackers
and to 2) challenge RGB trackers with sequences for which the depth cue is
essential. This work introduces a new RGBD tracking dataset - Depth-Track -
that has twice as many sequences (200) and scene types (40) than in the largest
existing dataset, and three times more objects (90). In addition, the average
length of the sequences (1473), the number of deformable objects (16) and the
number of annotated tracking attributes (15) have been increased. Furthermore,
by running the SotA RGB and RGBD trackers on DepthTrack, we propose a new RGBD
tracking baseline, namely DeT, which reveals that deep RGBD tracking indeed
benefits from genuine training data. The code and dataset is available at
this https URL

该论文介绍了一种新的 RGBD 跟踪数据集 - Depth-Track，其中有 200 个序列，40 种场景类型和 90 个对象，并建立了一个新的 RGBD 跟踪基准 DeT，揭示了真正的训练数据对深度 RGBD 跟踪的益处。