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

Alan Lukezic, Ugur Kart, Jani Kapyla, Ahmed Durmush, Joni-Kristian Kamarainen, Jiri Matas, Matej Kristan; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 10013-10022


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.

Related Material

author = {Lukezic, Alan and Kart, Ugur and Kapyla, Jani and Durmush, Ahmed and Kamarainen, Joni-Kristian and Matas, Jiri and Kristan, Matej},
title = {CDTB: A Color and Depth Visual Object Tracking Dataset and Benchmark},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}