DepthTrack: Unveiling the Power of RGBD Tracking

Song Yan1; Jinyu Yang2; Jani Käpylä1; Feng Zheng2; Ales Leonardis3; Joni-Kristian Kamarainen1

2021

ICCV

1550-5499

978-1-6654-2813-2

2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)

10705-10713

2021

Virtual-only Conference

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

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 - DepthTrack - 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 https://github.com/xiaozai/DeT.

Datasets and evaluation Motion and tracking

其他

英语

CPCI-S ; EI

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods

WOS:000798743200069

20221511951435

人工提交

1.Tampere University
2.Southern University of Science and Technology
3.University of Birmingham

Song Yan,Jinyu Yang,Jani Käpylä,et al. DepthTrack: Unveiling the Power of RGBD Tracking[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:10705-10713.