The best RGBD trackers provide high accuracy but are slow to run. On the other hand, the best RGB trackers are fast but clearly inferior on the RGBD datasets. In this work, we propose a deep depth-aware long-term tracker that achieves state-of-the-art RGBD tracking performance and is fast to run. We reformulate deep discriminative correlation filter (DCF) to embed the depth information into deep features. Moreover, the same depth-aware correlation filter is used for target re-detection. Comprehensive evaluations show that the proposed tracker achieves state-of-the-art performance on the Princeton RGBD, STC, and the newly-released CDTB benchmarks and runs 20 fps.
@article{arxiv.1912.00660,
title = {DAL -- A Deep Depth-aware Long-term Tracker},
author = {Yanlin Qian and Alan Lukežič and Matej Kristan and Joni-Kristian Kämäräinen and Jiri Matas},
journal= {arXiv preprint arXiv:1912.00660},
year = {2019}
}