[1]LIN Shubin,WU Guishan,YANG Wenyuan.Target-aware Transformer unmanned aerial vehicle tracker: a focus on key information[J].CAAI Transactions on Intelligent Systems,2025,20(6):1483-1492.[doi:10.11992/tis.202506030]
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Target-aware Transformer unmanned aerial vehicle tracker: a focus on key information

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 6 Page number: 1483-1492 Column: 学术论文—机器人 Public date: 2025-11-05
Title:
Target-aware Transformer unmanned aerial vehicle tracker: a focus on key information
Author(s):
LIN Shubin1; 2;  WU Guishan1; 2;  YANG Wenyuan3
1. School of Computer Science, Minnan Normal University, Zhangzhou 363000, China;
2. Fujian Province Universities Key Laboratory of Data Science and Intelligence Application, Minnan Normal University, Zhangzhou 363000, China;
3. Fujian Key Laborator
Keywords:
target-tracking; Transformer; adaptive token termination; tracking framework; feature aggregation; unmanned aerial vehicle; background suppression; benchmark
CLC:
TP391.4
DOI:
10.11992/tis.202506030
Abstract:
Unmanned aerial vehicle (UAV) visual tracking is a foundational technology in the field of UAV applications. Existing UAV tracking methods focus on the input search area for learning, leading to a decline in feature discrimination and difficulty in dealing with complex background interference in UAV scenarios. This paper proposes a target-aware Transformer UAV tracker that focuses on key information. First, a single-stream tracking framework integrating feature learning and target search is constructed to enhance the information interaction between tokens. Second, an adaptive relationship modeling mechanism is proposed. This mechanism models the relationship between the target template and the search area tokens and dynamically classifies them. As a result, the processing of background tokens is prematurely terminated, and the focus shifts to key target information. A feature aggregation module has been developed to retain the detailed features of the target, enhance the discriminative power of the feature representation, and introduce temporal consistency constraints to ensure the stability of features. Experiments on the UAV123, DTB70, and UavDrak135 UAV tracking benchmarks demonstrate that the proposed algorithm exhibits superior performance in UAV tracking.
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