DepthTrack: Unveiling the Power of RGBD Tracking

Song Yan, Jinyu Yang, Jani Käpylä, Feng Zheng, Aleš Leonardis, Joni-Kristian Kämäräinen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
PublisherIEEE
Pages10705-10713
Number of pages9
ISBN (Electronic)9781665428125
DOIs
Publication statusPublished - 2021
Publication typeA4 Article in conference proceedings
EventIEEE International Conference on Computer Vision - , Canada
Duration: 11 Sep 202117 Sep 2021

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
ISSN (Print)1550-5499

Conference

ConferenceIEEE International Conference on Computer Vision
Country/TerritoryCanada
Period11/09/2117/09/21

Publication forum classification

  • Publication forum level 2

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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