@inproceedings{4cd49b1e6b0140738312f772d8b8e78e,
title = "DAL: A Deep Depth-Aware Long-term Tracker",
abstract = "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 redetection. 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.",
author = "Song Yan and Yanlin Qian and Alan Luke{\v z}i{\v c} and Matej Kristan and Joni-Kristian K{\"a}m{\"a}r{\"a}inen and Ji{\v r}{\'i} Matas",
note = "JUFOID=58099; International Conference on Pattern Recognition ; Conference date: 10-01-2021 Through 15-01-2021",
year = "2020",
doi = "10.1109/ICPR48806.2021.9412984",
language = "English",
isbn = "978-1-7281-8809-6",
series = "International Conference on Pattern Recognition",
publisher = "IEEE",
pages = "7825--7832",
booktitle = "2020 25th International Conference on Pattern Recognition (ICPR)",
address = "United States",
}