ORCID Profile
0000-0001-9403-1112
Current Organisation
Xidian University
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Publisher: Springer Science and Business Media LLC
Date: 12-04-2017
Publisher: Elsevier BV
Date: 06-2022
Publisher: IEEE
Date: 12-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Association for Computing Machinery (ACM)
Date: 04-03-2022
DOI: 10.1145/3486678
Abstract: Tracking in the unmanned aerial vehicle (UAV) scenarios is one of the main components of target-tracking tasks. Different from the target-tracking task in the general scenarios, the target-tracking task in the UAV scenarios is very challenging because of factors such as small scale and aerial view. Although the discriminative correlation filter (DCF)-based tracker has achieved good results in tracking tasks in general scenarios, the boundary effect caused by the dense s ling method will reduce the tracking accuracy, especially in UAV-tracking scenarios. In this work, we propose learning an adaptive spatial-temporal context-aware (ASTCA) model in the DCF-based tracking framework to improve the tracking accuracy and reduce the influence of boundary effect, thereby enabling our tracker to more appropriately handle UAV-tracking tasks. Specifically, our ASTCA model can learn a spatial-temporal context weight, which can precisely distinguish the target and background in the UAV-tracking scenarios. Besides, considering the small target scale and the aerial view in UAV-tracking scenarios, our ASTCA model incorporates spatial context information within the DCF-based tracker, which could effectively alleviate background interference. Extensive experiments demonstrate that our ASTCA method performs favorably against state-of-the-art tracking methods on some standard UAV datasets.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 12-2018
Publisher: Elsevier BV
Date: 04-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 03-04-2020
Abstract: Existing deep Thermal InfraRed (TIR) trackers usually use the feature models of RGB trackers for representation. However, these feature models learned on RGB images are neither effective in representing TIR objects nor taking fine-grained TIR information into consideration. To this end, we develop a multi-task framework to learn the TIR-specific discriminative features and fine-grained correlation features for TIR tracking. Specifically, we first use an auxiliary classification network to guide the generation of TIR-specific discriminative features for distinguishing the TIR objects belonging to different classes. Second, we design a fine-grained aware module to capture more subtle information for distinguishing the TIR objects belonging to the same class. These two kinds of features complement each other and recognize TIR objects in the levels of inter-class and intra-class respectively. These two feature models are learned using a multi-task matching framework and are jointly optimized on the TIR tracking task. In addition, we develop a large-scale TIR training dataset to train the network for adapting the model to the TIR domain. Extensive experimental results on three benchmarks show that the proposed algorithm achieves a relative gain of 10% over the baseline and performs favorably against the state-of-the-art methods. Codes and the proposed TIR dataset are available at github.com/QiaoLiuHit/MMNet.
Publisher: Springer Science and Business Media LLC
Date: 02-04-2019
Publisher: Informa UK Limited
Date: 15-02-2022
Publisher: Elsevier BV
Date: 02-2022
Publisher: IEEE
Date: 12-2018
Publisher: IEEE
Date: 12-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 12-10-2023
Publisher: Springer Science and Business Media LLC
Date: 17-06-2019
Publisher: Elsevier BV
Date: 04-2020
Publisher: Elsevier BV
Date: 05-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 02-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Publisher: Springer Science and Business Media LLC
Date: 11-06-2020
Publisher: Springer Science and Business Media LLC
Date: 03-11-2019
Publisher: Atlantis Press International BV
Date: 26-09-2024
Publisher: SPIE-Intl Soc Optical Eng
Date: 13-05-2017
Publisher: Springer Science and Business Media LLC
Date: 07-09-2019
Publisher: Elsevier BV
Date: 10-2020
Start Date: 2023
End Date: 2025
Funder: National Natural Science Foundation of China
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