[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]
点击复制

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.
参考文献/References:
[1] SPURNY V, PETRLIK M, VONASEK V, et al. Cooperative transport of large objects by a pair of unmanned aerial systems using sampling-based motion planning[C]//2019 24th IEEE International Conference on Emerging Technologies and Factory Automation. Zaragoza: IEEE, 2019: 955-962.
[2] HU Chaofang, ZHANG Zelong, YANG Na, et al. Fuzzy multiobjective cooperative surveillance of multiple UAVs based on distributed predictive control for unknown ground moving target in urban environment[J]. Aerospace science and technology, 2019, 84: 329–338.
[3] LIN Shanggang, GARRATT M A, LAMBERT A J. Monocular vision-based real-time target recognition and tracking for autonomously landing an UAV in a cluttered shipboard environment[J]. Autonomous robots, 2017, 41(4): 881–901.
[4] XUAN Shiyu, LI Shengyang, ZHAO Zifei, et al. Rotation adaptive correlation filter for moving object tracking in satellite videos[J]. Neurocomputing, 2021, 438: 94–106.
[5] BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco: IEEE, 2010: 2544-2550.
[6] HENRIQUES J F, CASEIRO R, MARTINS P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(3): 583–596.
[7] DANELLJAN M, BHAT G, KHAN F S, et al. ECO: efficient convolution operators for tracking[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 6931-6939.
[8] 戴煜彤, 陈志国, 傅毅. 相关滤波的运动目标抗遮挡再跟踪技术[J]. 智能系统学报, 2021, 16(4): 630–640
DAI Yutong, CHEN Zhiguo, FU Yi. Anti-occlusion retracking technology for a moving target based on correlation filtering[J]. CAAI transactions on intelligent systems, 2021, 16(4): 630–640
[9] 李玺, 查宇飞, 张天柱, 等. 深度学习的目标跟踪算法综述[J]. 中国图象图形学报, 2019, 24(12): 2057–2080
LI Xi, ZHA Yufei, ZHANG Tianzhu, et al. Survey of visual object tracking algorithms based on deep learning[J]. Journal of image and graphics, 2019, 24(12): 2057–2080
[10] 朱建章, 王栋, 卢湖川. 背景与时间感知的相关滤波实时视觉跟踪[J]. 中国图象图形学报, 2019, 24(4): 536–549
ZHU Jianzhang, WANG Dong, LU Huchuan. Learning background-temporal-aware correlation filter for real-time visual tracking[J]. Journal of image and graphics, 2019, 24(4): 536–549
[11] GALOOGAHI H K, FAGG A, LUCEY S. Learning background-aware correlation filters for visual tracking[C]//2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017 : 1144-1152.
[12] DANELLJAN M, H?GER G, KHAN F S, et al. Learning spatially regularized correlation filters for visual tracking[C]//2015 IEEE International Conference on Computer Vision. Santiago: IEEE, 2015: 4310-4318.
[13] DAI Kenan, WANG Dong, LU Huchuan, et al. Visual tracking via adaptive spatially-regularized correlation filters[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach: IEEE, 2019 : 4665-4674.
[14] 刘威, 靳宝, 周璇, 等. 基于特征融合及自适应模型更新的相关滤波目标跟踪算法[J]. 智能系统学报, 2020, 15(4): 714–721
LIU Wei, JIN Bao, ZHOU Xuan, et al. Correlation filter target tracking algorithm based on feature fusion and adaptive model updating[J]. CAAI transactions on intelligent systems, 2020, 15(4): 714–721
[15] 张开华, 樊佳庆, 刘青山. 视觉目标跟踪十年研究进展[J]. 计算机科学, 2021, 48(3): 40–49
ZHANG Kaihua, FAN Jiaqing, LIU Qingshan. Advances on visual object tracking in past decade[J]. Computer science, 2021, 48(3): 40–49
[16] LI Yiming, FU Changhong, DING Fangqiang, et al. Augmented memory for correlation filters in real-time UAV tracking[C]//2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York: ACM, 2020: 1559-1566.
[17] FU Changhong, LIN Fuling, LI Yiming, et al. Correlation filter-based visual tracking for UAV with online multi-feature learning[J]. Remote sensing, 2019, 11(5): 549.
[18] 周士琪, 王耀南, 钟杭. 融合视觉显著性再检测的孪生网络无人机目标跟踪算法[J]. 智能系统学报, 2021, 16(3): 584–594
ZHOU Shiqi, WANG Yaonan, ZHONG Hang. Siamese network combined with visual saliency re-detection for UAV object tracking[J]. CAAI transactions on intelligent systems, 2021, 16(3): 584–594
[19] LI Yiming, FU Changhong, DING Fangqiang, et al. AutoTrack: towards high-performance visual tracking for UAV with automatic spatio-temporal regularization[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE, 2020 : 11920-11929.
[20] FU Changhong, HUANG Ziyuan, LI Yiming, et al. Boundary effect-aware visual tracking for UAV with online enhanced background learning and multi-frame consensus verification[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Macao: IEEE, 2019: 4415-4422.
[21] FU Changhong, XIONG Weijiang, LIN Fuling, et al. Surrounding-aware correlation filter for UAV tracking with selective spatial regularization[J]. Signal processing, 2020, 167: 107324.
[22] HUANG Ziyuan, FU Changhong, LI Yiming, et al. Learning aberrance repressed correlation filters for real-time UAV tracking[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul: IEEE, 2019: 2891-2900.
[23] FU Changhong, YANG Xiaoxiao, LI Fan, et al. Learning consistency pursued correlation filters for real-time UAV tracking[C]//2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Las Vegas: IEEE, 2020 : 8293-8300.
[24] LIN Fuling, FU Changhong, HE Yujie, et al. BiCF: learning bidirectional incongruity-aware correlation filter for efficient UAV object tracking[C]//2020 IEEE International Conference on Robotics and Automation. Paris: IEEE, 2020 : 2365-2371.
[25] LI Bowen, FU Changhong, DING Fangqiang, et al. All-day object tracking for unmanned aerial vehicle[J]. IEEE transactions on mobile computing[EB/OL]. (2021-01-21)[ 2021-10-20]. https://arxiv.org/abs/2101.08446.
相似文献/References:
[1]夏 凡,王 宏.基于局部异常行为检测的欺骗识别研究[J].智能系统学报,2007,2(5):12.
　XIA Fan,WANG Hong.Methodologies for deception detection based on abnormal b ehavior[J].CAAI Transactions on Intelligent Systems,2007,2():12.
[2]王绍钰 蔡自兴,陈爱斌.改进的粒子滤波器目标跟踪方法[J].智能系统学报,2008,3(3):189.
　WANG Shao-yu,CAI Zi-xing,CHEN Ai-bin.Improved object tracking method for particle filters[J].CAAI Transactions on Intelligent Systems,2008,3():189.
[3]刘 清,吴志刚,窦 琴,等.粒子滤波的视频目标跟踪算法研究[J].智能系统学报,2009,4(6):538.[doi:10.3969/j.issn.1673-4785.2009.06.012]
　LIU Qing,WU Zhi-gang,DOU Qin,et al.A particle filtering algorithm for tracking moving objects in videos[J].CAAI Transactions on Intelligent Systems,2009,4():538.[doi:10.3969/j.issn.1673-4785.2009.06.012]
[4]伍 明,孙继银.一种机器人未知环境下动态目标跟踪交互多模滤波算法[J].智能系统学报,2010,5(2):127.
　WU Ming,SUN Ji-yin.An interacting multiple model filtering algorithm for mobile robots to improve tracking of moving objects in unknown environments[J].CAAI Transactions on Intelligent Systems,2010,5():127.
[5]杨 戈,刘 宏.视觉跟踪算法综述[J].智能系统学报,2010,5(2):95.
　YANG Ge,LIU Hong.Survey of visual tracking algorithms[J].CAAI Transactions on Intelligent Systems,2010,5():95.
[6]李 金,胡文广.基于颜色的快速人体跟踪及遮挡处理[J].智能系统学报,2010,5(4):353.
　LI Jin,HU Wen-guang.Tracking fast movement using colors while accommodating occlusion[J].CAAI Transactions on Intelligent Systems,2010,5():353.
[7]刘宏,李哲媛,许超.视错觉现象的分类和研究进展[J].智能系统学报,2011,6(1):1.
　LIU Hong,LI Zheyuan,XU Chao.The categories and research advances of visual illusions[J].CAAI Transactions on Intelligent Systems,2011,6():1.
[8]刘侠,陶冶,邢春.统计差分与自启动的Camshift跟踪算法[J].智能系统学报,2011,6(4):355.
　LIU Xia,TAO Ye,XING Chun.An objective tracking Camshift algorithm based onautomatic startup and the statistical differential method[J].CAAI Transactions on Intelligent Systems,2011,6():355.
[9]叶果,程洪,赵洋.电影中吸烟活动识别[J].智能系统学报,2011,6(5):440.
　YE Guo,CHENG Hong,ZHAO Yang.moking recognition in movies[J].CAAI Transactions on Intelligent Systems,2011,6():440.
[10]史晓鹏,何为,韩力群.采用Hough变换的道路边界检测算法[J].智能系统学报,2012,7(1):81.
　SHI Xiaopeng,HE Wei,HAN Liqun.A road edge detection algorithm based on the Hough transform[J].CAAI Transactions on Intelligent Systems,2012,7():81.
[11]刘威,靳宝,周璇,等.基于特征融合及自适应模型更新的相关滤波目标跟踪算法[J].智能系统学报,2020,15(4):714.[doi:10.11992/tis.201803036]
　LIU Wei,JIN Bao,ZHOU Xuan,et al.Correlation filter target tracking algorithm based on feature fusion and adaptive model updating[J].CAAI Transactions on Intelligent Systems,2020,15():714.[doi:10.11992/tis.201803036]
[12]吴贵山,林淑彬,钟江华,等.区域损失函数的孪生网络目标跟踪[J].智能系统学报,2020,15(4):722.[doi:10.11992/tis.201910005]
　WU Guishan,LIN Shubin,ZHONG Jianghua,et al.Regional loss function based siamese network for object tracking[J].CAAI Transactions on Intelligent Systems,2020,15():722.[doi:10.11992/tis.201910005]
[13]林椹尠,郑兴宁,吴成茂.结合模糊特征检测的鲁棒核相关滤波跟踪法[J].智能系统学报,2021,16(2):323.[doi:10.11992/tis.201912010]
　LIN Zhenxian,ZHENG Xingning,WU Chengmao.Robust KCF tracking algorithm combined with fuzzy feature detection[J].CAAI Transactions on Intelligent Systems,2021,16():323.[doi:10.11992/tis.201912010]
[14]周士琪,王耀南,钟杭.融合视觉显著性再检测的孪生网络无人机目标跟踪算法[J].智能系统学报,2021,16(3):584.[doi:10.11992/tis.202101035]
　ZHOU Shiqi,WANG Yaonan,ZHONG Hang.Siamese network combined with visual saliency re-detection for UAV object tracking[J].CAAI Transactions on Intelligent Systems,2021,16():584.[doi:10.11992/tis.202101035]
[15]姜文涛,张大鹏.优化分类的弱目标孪生网络跟踪研究[J].智能系统学报,2023,18(5):984.[doi:10.11992/tis.202211043]
　JIANG Wentao,ZHANG Dapeng.Research on weak object tracking based on Siamese network with optimized classification[J].CAAI Transactions on Intelligent Systems,2023,18():984.[doi:10.11992/tis.202211043]
[16]黄昱程,肖子旺,武丹凤,等.时空融合与判别力增强的孪生网络目标跟踪方法[J].智能系统学报,2024,19(5):1218.[doi:10.11992/tis.202306005]
　HUANG Yucheng,XIAO Ziwang,WU Danfeng,et al.Spatiotemporal fusion and discriminative augmentation for improved Siamese tracking[J].CAAI Transactions on Intelligent Systems,2024,19():1218.[doi:10.11992/tis.202306005]

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

更新日期/Last Update: 1900-01-01
Copyright © 《 智能系统学报》 编辑部
地址：（150001）黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话：0451- 82534001、82518134　邮箱：tis@vip.sina.com