@inproceedings{4a4179b48aec4520aaba2095c2d0b42b,
title = "Improving ADNet for Robust Tracking",
abstract = "By taking advantages of the multi-domain structure and efficient searching strategy, ADNet (Action Decision Network) outperforms other approaches. However, its precision is limited due to the lack of detail feature caused by the improper network structure and other issues. Through our improvements, the proposed approach is more accurate than ADNet. Experiments demonstrate the advantages of our method.",
keywords = "Deep Learning, Visual Tracking",
author = "Tiansa Zhang and Bo Wang and Zhiqiang Zhou and Zhe An",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 31st Chinese Control and Decision Conference, CCDC 2019 ; Conference date: 03-06-2019 Through 05-06-2019",
year = "2019",
month = jun,
doi = "10.1109/CCDC.2019.8832736",
language = "English",
series = "Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3347--3351",
booktitle = "Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019",
address = "United States",
}