基于视觉显著性的无人机目标跟踪

doi:10.3969/j.issn.0253-2778.2020.08.017

中国科学技术大学学报 ›› 2020, Vol. 50 ›› Issue (8): 1162-1169.DOI: 10.3969/j.issn.0253-2778.2020.08.017

基于视觉显著性的无人机目标跟踪

李鹏，郑宇，张谈贵

1.中航机载系统有限公司,北京 100028；2.北京临近空间飞行器系统工程研究所，北京 100076 3.甘肃省核与辐射安全中心，安徽合肥 230027

收稿日期:2020-06-23 修回日期:2020-08-26 接受日期:2020-08-26 出版日期:2020-08-31 发布日期:2020-08-26

UAV target tracking based on visual attention mechanism

LI Peng, ZHENG Yu2, ZHANG Tangui3

Received:2020-06-23 Revised:2020-08-26 Accepted:2020-08-26 Online:2020-08-31 Published:2020-08-26
Contact: ZHANG Tangui
About author:LI Peng, male, born in 1981, Master candidate. Research field: Graph processing, target locating and tracking. E-mail: lipeng201905@126.com

摘要/Abstract

摘要： 近年来，小型无人机在无卫星导航条件下的使用需求日益强烈.针对多目标识别问题，提出一种基于小型多旋翼无人机平台的多运动目标识别定位技术，首先采用一种先快速定位感兴趣区域基于视觉注意机制，再用机器学习算法分析感兴趣区域获取目标的方法.实现了对图像中的指定目标进行追踪，同时还实现对目标的定位，其定位误差小于15 cm.该方法有效降低了光照变化、运动模糊、颜色类似物干扰及复杂背景等因素的影响.以地面机器人作为追踪目标进行算法测试验证，在目标消失时间较短的情况下，能够达到较好的追踪效果.

关键词: 无人机, 目标检测, 视觉注意机制, 机器学习

Abstract: In recent years, the demand for small Unmanned Aerial Vehicles (UAV) in GPS-denied environment is increasingly strong. To solve the problem of multi-target recognition, we study the multi moving target recognition and location technology based on the platform of the small multi-rotor UAV. We used a method to quickly locate the region of interest based on the visual attention mechanism, and then used the machine learning algorithm to classify the region of interest to obtain the target accurately. Our method can track the specified target in the image and locate the target in real time, which the algorithm delay is about 50ms and the location error is less than 15 cm. Our solution can effectively reduce the influence of light variation, motion blur, the color analogue interference and complex background. The ground robot is used as the tracking target to test and verify the algorithm, which can achieve a better tracking effect.

Key words: unmanned Aerial Vehicle, target tracking, visual attention mechanism, machine learning

中图分类号:

TP18

李鹏，郑宇，张谈贵. 基于视觉显著性的无人机目标跟踪[J]. 中国科学技术大学学报, 2020, 50(8): 1162-1169.

参考文献

［1］ FORSYTH D A, PONCE J. Computer Vision: A Modern Approach[M]. Second Edition. New Jersey: Prentice Hall, 2012: 255-261.
[2] COMANICIU D, MEER P. Mean shift: A robust approach toward feature space analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 603-619.
[3] NING J F, ZHANG L, ZHANG D, et al. Interactive image Segmentation by maximal similarity based region merging[J]. Pattern Recognition, 2010, 43(2): 445-456.
[4] MEDEIROS F N S, CARVALHO E A, USHIZIMA D M, et al. SAR imagery segmentation by statistical region growing and hierarchical merging[J]. Digital Signal Processing, 2010, 20(5): 1365-1378.
[5] BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters[C]// Computer Vision and Pattern Recognition. IEEE, 2010:2544-2550.
[6] UGARRIZA L G, SABER E, VANTARAM S R, et al. Automatic image segmentation by dynamic region growth and multiresolution merging[J]. IEEE Transactions on Image Processing, 2009, 18(10): 2275-2288.
[7] ZHAI W X, CHENG C Q. A camshift motion tracking algorithm based on kalman filter[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2015, 51(5):799-804.
[8] YU W. Object tracking with particle filter in UAV video[J]. Proceedings of SPIE-The International Society for Optical Engineering, 2013, 8918(48):10.
[9] LI Z H, WANG L, CUI J G. Weak aerial target tracking algorithm based on Camshift and Particle Filter[J]. Computer Engineering and Applications, 2011, 47(9):192-195.
[10] ZHAO P, SHEN T, SHAN B. An object tracking algorithm for TV guiding systemof UAV based on particle filter[J]. Optics and Precision Engineering, 2008, 1: 026.
[11] ZHAO P, SHEN T, SHAN B. An object tracking algorithm for TV guiding system of UAV based on particle filter[J]. Optics and Precision Engineering, 2008, 1: 026.
[12] WANG W, MENG Z H. Target tracking base on improved Camshift algorithm[J]. Information Technology, 2015(1):85-88.
[13] CHANG L, DUARTE M M, SUCAR L E, et al. A Bayesian approach for object classification based on clusters of SIFT local features[J]. Expert Systems with Applications, 2012, 39(2): 1679-1686.
[14] YU G, YING X. Architecture design of deep convolutional neural network for SAR target recognition[J]. Journal of Image and Graphics, 2018.
[15] RUSSAKOVSKY O, DENG J. Imagenet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015, 115(3):211-252.
[16] UIJLINGS J R R, SANDE K E A, GEVERS T. Selective search for object recognition[J]. International Journal of Computer Vision, 2013.
[17] REDMON J, FARHADI A. YOLO9000: Better, Faster, Stronger [C]// IEEE Conference on Computer Vision & Pattern Recognition. IEEE, 2017:6517-6525.
[18] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]// IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2005, 1: 886-893.
[19] ZHAO B, WU X, FENG J, et al. Diversified visual attention networks for fine-grained object classification[J]. IEEE Transactions on Multimedia, 2017, 19(6):1245-1256.
[20] ITTI L, KOCH C. Computational modelling of visual attention[J]. Nature Reviews Neuroence, 2001, 2(3):194-203.
[21] HOU X, ZHANG L. Saliency detection: A spectral residual approach[C]// IEEE Conference on Computer Vision and Pattern Recognition.IEEE, 2007:11-19.
[22] HEMAMI S, ESTRADA F, SUSSTRUNK S. Frequency-tuned salient region detection[C]// IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2009). IEEE, 2009:1597-1604.
[23] GOFERMAN S, ZELNIKMANOR L, TAL A. Context-aware saliency detection[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2012, 34(10):1915-26.
[24] LIU Z, LE M, LUO S. Superpixel-based saliency detection[J]. International Workshop on Image Analysis for Multimedia Interactive Services, 2013, 8215(2):1-4.
[25] HAREL J, KOCH C, PERONA P. Graph-based visual saliency[C]. Proceedings of Neural Information Processing Systems (NIPS), 2007.
[26] KLETTE R. Concise Computer Vision-An Introduction into Theory and Algorithms[M]. Springer London, 2014.
[27] Chang C C, Lin C J. LIBSVM: A library for support vector machines[J]. ACM Transactions on Intelligent Systems & Technology, 2011, 2(3):389-396.
[28] HOFMANN T, SCHLKOPF B, SMOLA A J. Kernel methods in machine learning[J]. Annals of Statistics, 2008, 36(3):1171-1220.
[29] ZHANG Z. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334.

()
()

[1]	吴世崇，廖飞，吴文华，付在明. 基于集成建模方法的四旋翼通用控制器设计[J]. 中国科学技术大学学报, 2020, 50(8): 1084-1092.
[2]	高翔，陈力. 分组随机梯度下降法：掉队和延迟的平衡[J]. 中国科学技术大学学报, 2020, 50(8): 1156-1161.
[3]	李健聪，王泰然，舒武，胡素磊，欧阳润海，李微雪. 基于人工智能的描述符预测合金催化材料形成能[J]. 中国科学技术大学学报, 2020, 50(6): 844-851.
[4]	龙飞, 黄琨, 李峰. 一种机器学习与相变之间的新型映射[J]. 中国科学技术大学学报, 2020, 50(1): 18-28.
[5]	谭嘉莉，何钰，吴燕晶，孙广中. 应用于校园心理咨询的对话匹配度预测模型[J]. 中国科学技术大学学报, 2018, 48(9): 739-747.
[6]	李勇明，肖洁，王品，颜芳. 基于封装式偏差回归的心脏生理和病理年龄估计算法[J]. 中国科学技术大学学报, 2018, 48(9): 762-769.
[7]	林宇鹏，谢智歌，徐凯，陈飞，刘利刚. 基于超限学习机的快速癌症检测方法[J]. 中国科学技术大学学报, 2018, 48(2): 154-160.
[8]	陈东杰，张文生，杨阳. 基于深度学习的高铁接触网定位器检测与识别[J]. 中国科学技术大学学报, 2017, 47(4): 320-327.
[9]	张莉，卢星凝，陆从林，王邦军，李凡长. 支持向量机在高考成绩预测分析中的应用[J]. 中国科学技术大学学报, 2017, 47(1): 1-9.

基于视觉显著性的无人机目标跟踪

UAV target tracking based on visual attention mechanism

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 9

编辑推荐

Metrics

本文评价