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CDTB: A Color and Depth Visual Object Tracking Dataset and Benchmark

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Details

Original languageEnglish
Title of host publication2019 International Conference on Computer Vision, ICCV 2019
PublisherIEEE
Pages10012-10021
Number of pages10
ISBN (Electronic)9781728148038
ISBN (Print)978-1-7281-4804-5
DOIs
Publication statusPublished - 2019
Publication typeA4 Article in a conference publication
EventIEEE/CVF International Conference on Computer Vision -
Duration: 27 Oct 20192 Nov 2019

Publication series

NameIEEE International Conference on Computer Vision
ISSN (Print)1550-5499
ISSN (Electronic)2380-7504

Conference

ConferenceIEEE/CVF International Conference on Computer Vision
Period27/10/192/11/19

Abstract

We propose a new color-and-depth general visual object tracking benchmark (CDTB). CDTB is recorded by several passive and active RGB-D setups and contains indoor as well as outdoor sequences acquired in direct sunlight. The CDTB dataset is the largest and most diverse dataset in RGB-D tracking, with an order of magnitude larger number of frames than related datasets. The sequences have been carefully recorded to contain significant object pose change, clutter, occlusion, and periods of long-term target absence to enable tracker evaluation under realistic conditions. Sequences are per-frame annotated with 13 visual attributes for detailed analysis. Experiments with RGB and RGB-D trackers show that CDTB is more challenging than previous datasets. State-of-the-art RGB trackers outperform the recent RGB-D trackers, indicating a large gap between the two fields, which has not been previously detected by the prior benchmarks. Based on the results of the analysis we point out opportunities for future research in RGB-D tracker design.

Publication forum classification

Field of science, Statistics Finland