[1]林淑彬,吴贵山,姚文勇,等.基于光照自适应动态一致性的无人机目标跟踪[J].智能系统学报,2022,17(6):1093-1103.[doi:10.11992/tis.202110023]
　LIN Shubin,WU Guishan,YAO Wenyong,et al.Unmanned aerial vehicles object tracking based on illumination adaptive dynamic consistency[J].CAAI Transactions on Intelligent Systems,2022,17(6):1093-1103.[doi:10.11992/tis.202110023]
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基于光照自适应动态一致性的无人机目标跟踪

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 17 期数: 2022年第6期 页码: 1093-1103 栏目: 学术论文—机器学习 出版日期: 2022-11-05
Title:
Unmanned aerial vehicles object tracking based on illumination adaptive dynamic consistency
作者:
林淑彬1,2, 吴贵山1,2, 姚文勇3, 杨文元4
1. 闽南师范大学 计算机学院，福建 漳州 363000;
2. 闽南师范大学 数据科学与智能应用福建省高校重点实验室，福建 漳州 363000;
3. 闽南师范大学 外国语学院，福建 漳州 363000;
4. 闽南师范大学 福建省粒计算及其应用重点实验室，福建 漳州 363000
Author(s):
LIN Shubin1,2, WU Guishan1,2, YAO Wenyong3, YANG Wenyuan4
1. School of Computer Science, Minnan Normal University, Zhangzhou 363000, China;
2. Key Laboratory of Data Science and Intelligence Application of Fujian Provincial Universities, Minnan Normal University, Zhangzhou 363000, China;
3. School of Foreign Studies, Minnan Normal University, Zhangzhou 363000, China;
4. Fujian Key Laboratory of Granular Computing and Application, Minnan Normal University, Zhangzhou 363000, China
关键词:
计算机视觉; 目标跟踪; 无人机; 机器学习; 相关滤波; 光照自适应; 动态约束; 一致性评估
Keywords:
computer vision; object tracking; unmanned aerial vehicle; machine learning; correlation filtering; illumination adaptive; dynamic constraint; consistency evaluation
分类号:
TP391.4
DOI:
10.11992/tis.202110023
文献标志码:
2022-08-25
摘要:
无人机跟踪任务经常面临各种光线变化场景，然而无人机跟踪方法主要在光线充足下实现鲁棒跟踪。提出一种具有光照自适应性和跨帧语义感知动态一致性评估的无人机跟踪方法，实现光线不足下的无人机目标跟踪。首先构建光照自适应模块对昏暗场景进行识别，对视频图像的光照强度进行补偿；其次构建目标模板训练具有目标感知能力的滤波器进行相关运算，并利用跨帧之间的响应信息进行一致性评估；最后构建动态约束策略并对响应差异进行约束，使跟踪器保持时间平滑。在UAVDark135和UAV123数据集上，与9种先进算法进行对比实验，结果表明该算法具有较好的跟踪性能。
Abstract:
The tracking of unmanned aerial vehicles (UAVs) is often confronted with illumination change scenes. However, UAV tracking methods mainly achieve robust tracking under sufficient illumination. In this study, a UAV tracking method that uses dynamic consistency evaluation based on illumination adaptability and cross-frame semantic perception is proposed to realize efficient UAV object tracking under insufficient illumination. First, an adaptive illumination module was designed to recognize dim scenes and compensate for the illumination intensity of the dim image. Subsequently, aobject template was built to train a filter with object perception to perform correlation operations; furthermore, the consistency evaluation was performed using the response information between the cross-frames. Finally, a dynamic constraint strategy was developed, and the response difference of the tracker was constrained to maintain temporal smoothness. When the tracking performance of this method was compared to that of nine state-of-the-art algorithms on the UAVDark135 and UAV123 datasets, the results indicated that the algorithm had better tracking performance.
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备注/Memo

收稿日期:2021-10-20。
基金项目:国家自然科学青年基金项目（12101289）；福建省自然科学基金项目（2020J01821）.
作者简介:林淑彬，实验师，主要研究方向为计算机视觉和模式识别;吴贵山，高级实验师，主要研究方向为计算机视觉和机器学习。参与完成省自然科学基金项目2项。发表学术论文7篇;杨文元，教授，博士，主要研究方向为计算机视觉、模式识别和机器学习。主持省自然科学基金项目1项。发表学术论文20余篇
通讯作者:杨文元.E-mail:yangwycn@163.com

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