Articles

Aircraft Wingtip Tracking Algorithm Based on Oriented Gradient Local Binary Pattern

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  • 1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
    2. Engineering Technology Research Center of Flight Simulation & Advanced Training, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Received date: 2011-01-25

  Revised date: 2011-03-22

  Online published: 2011-11-24

Abstract

In order to provide security assurance for aircraft ground movement, wingtip tracking is proposed which is based on video image processing and analysis techniques. Due to the poor performance of the existing tracking features for a wingtip, this paper presents a new tracking algorithm within a particle filtering tracking framework which adopts oriented gradient local binary pattern(OG_LBP) descriptors to integrate texture and contour information. The OG_LBP descriptor not only reduces the histogram dimensionality but also expresses both local and holistic features of the wingtip image to enhance the discrimination. In addition, via combining the observation information of the current frame, the new tracking algorithm establishes a model of the target state. Furthermore, the radius of particle propagation is updated adaptively by the similarity between the target model and each hypothesis of the particle, which can overcome the degeneracy problem in particle filtering and result in low computational cost. Experimental results show that the proposed algorithm can achieve an accurate and robust tracking performance for different wingtips with complex background in real-time.

Cite this article

SUN Jin, GU Hongbin, HOU Jianbo . Aircraft Wingtip Tracking Algorithm Based on Oriented Gradient Local Binary Pattern[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2011 , 32(11) : 2062 -2072 . DOI: CNKI:11-1929/V.20110517.1516.005

References

[1] 田穆. FAA继续推进机场地面安全技术[J]. 国际航空, 2006(3): 31-32. Tian Mu. FAA aims to improve runway safety[J]. International Aviation, 2006(3): 31-32. (in Chinese)

[2] 郭晓静. 多传感器数据融合在飞机地面防撞中的应用[J]. 测控技术, 2008, 27(6): 15-17. Guo Xiaojing. Multi-sensor data fusion application in aircraft ground collision avoidance[J]. Measurement & Control Technology, 2008, 27(6): 15-17. (in Chinese)

[3] 万卫兵, 霍宏, 赵宇民. 智能视频监控中目标检测与识别[M]. 上海: 上海交通大学出版社, 2010. Wan Weibing, Huo Hong, Zhao Yuming. Object detection and recognition in intelligent visual surveillance. Shanghai: Shanghai Jiao Tong University Press, 2010. (in Chinese)

[4] 李培华. 一种新颖的基于颜色信息的粒子滤波器跟踪算法[J] .计算机学报, 2009, 32(12): 2455-2463. Li Peihua. A novel color based particle filter algorithm for object tracking[J]. Chinese Journal of Computer, 2009, 32(12): 2455-2463. (in Chinese)

[5] Katja N E, Esther K, Luc V G. An adaptive color-based particle filter[J]. Image and Vision Computing, 2002, 21(1): 99-110.

[6] Pérez P, Hue C, Vermaak J, et al. Color-based probabilistic tracking//Proceedings of the European Conference on Computer Vision. 2002:661-675.

[7] Isard M, Blake A. CONDENSATION—conditional density propagation for visual tracking [J]. International Journal on Computer Vision, 1998, 29(1): 5-28.

[8] 庄严, 战洪斌, 王伟, 等. 基于加权颜色直方图和粒子滤波的彩色物体跟踪[J]. 控制与决策, 2006, 21(8): 867-872. Zhuang Yan, Zhan Hongbin, Wang Wei, et al. Weighted color histogram based particle filter for visual target tracking[J]. Control and Decision, 2006, 21(8): 867-872. (in Chinese)

[9] 卢晓鹏, 殷学民, 邹谋炎. 一种基于颜色分布的混合视频跟踪方法[J]. 电子与信息学报, 2008, 30(2): 259-262. Lu Xiaopeng, Yin Xuemin, Zou Mouyan. A hybrid algorithm of object tracking based on color distribution[J]. Journal of Electronics & Information Technology, 2008, 30(2): 259-262. (in Chinese)

[10] Yogesh R, Namrata V, Allen T, et al. Tracking deforming objects using particle filtering for geometric active contours[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(8):1470-1475.

[11] 牛长锋, 陈登峰, 刘玉树. 基于SIFT特征和粒子滤波的目标跟踪方法[J]. 机器人, 2010, 32(2): 241-247. Niu Changfeng, Chen Dengfeng, Liu Yushu. Tacking object based on SIFT features and particle filter[J]. Robot, 2010, 32(2): 241-247. (in Chinese)

[12] 郑永斌, 黄新生, 丰松江. SIFT和旋转不变LBP相结合的图像匹配算法[J]. 计算机辅助设计与图形学学报, 2010, 22(2): 286-292. Zheng Yongbin, Huang Xinsheng, Feng Songjiang. An image matching algorithm based on combination of SIFT and the rotation invariant LBP[J]. Journal of Computer-Aided Design & Computer Graphics, 2010, 22(2): 286-292. (in Chinese)

[13] Shen C, Anton V d H, Dick A. Probabilistic multiple cue integration for particle filter based tracking//Proceedings of Digital Image Computing: Techniques and Applications. 2003, 1: 399-408.

[14] 李远征, 卢朝阳, 高全学, 等. 基于多特征融合的均值迁移粒子滤波跟踪算法[J].电子与信息学报, 2010, 32(2): 411-415. Li Yuanzheng, Lu Chaoyang, Gao Quanxue, et al. Particle filter and mean shift tracking method based on multi-feature fusion[J]. Journal of Electronics & Information Technology, 2010, 32(2): 411-415. (in Chinese)

[15] 高建坡, 王煜坚, 杨浩, 等. 以颜色和形状直方图为线索的粒子滤波人脸跟踪[J]. 中国图象图形学报, 2007, 12(3): 466-473. Gao Jianpo, Wang Yujian, Yang Hao, et al. Particle filter face tracking using color and shape histogram as clues[J]. Journal of Image and Graphics, 2007, 12(3): 466-473. (in Chinese)

[16] 高涛, 何明一, 戴玉超, 等. 多级LBP直方图序列特征的人脸识别[J]. 中国图象图形学报, 2009, 14(2): 202-207. Gao Tao, He Mingyi, Dai Yuchao, et al. Face recognition using multi-level histogram sequence local binary pattern[J]. Journal of Image and Graphics, 2009, 14(2): 202-207. (in Chinese)

[17] Timo O, Matti P, David H. A comparative study of texture measures with classification based on featured distributions [J]. Pattern Recognition, 1996, 29(1):51-59.

[18] Timo O, Matti P, Top M. Multiresolution gray scale and rotation invariant texture classification with local binary patterns[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.

[19] Navneet D, Bill T. Histograms of oriented gradients for human detection//Proceedings of Conference on Computer Vision and Pattern Recognition. 2005: 886-893.

[20] 孙瑾, 顾宏斌, 郑吉平. 一种基于梯度方向信息的运动目标检测算法[J]. 中国图象图形学报, 2008, 13(3): 571-579. Sun Jin, Gu Hongbin, Zheng Jiping. A gradient direction based moving object detection[J]. Journal of Image and Graphics, 2008, 13(3): 571-579. (in Chinese)

[21] Marko H, Matti P, Cordelia S. Description of interest regions with center-symmetric local binary patterns [J]. Pattern Recognition, 2009, 42(3): 58-69.

[22] 吴成茂, 田小平, 谭铁牛. 二维Otsu阈值法的快速迭代算法[J]. 模式识别与人工智能, 2008, 21(6): 746-757. Wu Chengmao, Tian Xiaoping, Tan Tieniu. Fast iterative algorithm for two-dimensional otsu thresholding method[J]. Pattern Recognition and Artificial Intelligence, 2008, 21(6): 746-757. (in Chinese)

[23] Sanjeev A, Simon M, Neil G, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking [J]. IEEE Transactions on Signal Processing, 2002, 50(2): 174-188.

[24] 胡士强, 敬忠良. 粒子滤波原理及其应用[M]. 北京:科学出版社, 2010:21-27. Hu Shiqiang, Jin Zhongliang. The theory and application of particle filter[M]. Beijing: Science Press, 2010:21-27. (in Chinese)

[25] 朱志宇. 粒子滤波算法及其应用原理[M]. 北京: 科学出版社, 2010. Zhu Zhiyu. Particle filtering and its application[M]. Beijing: Science Press, 2010. (in Chinese)

[26] 姚剑敏. 粒子滤波跟踪方法研究. 北京: 中国科学院研究生院, 2004. Yao Jianmin. Study on particle filtering based visual tracking method. Beijing: Graduate University of Chinese Academy of Science, 2004. (in Chinese)

[27] 胡昭华. 基于粒子滤波的视频目标跟踪技术研究. 南京: 南京理工大学, 2008: 16-19. Hu Shaohua. Visual target tracking based on particle filter[M]. Nanjing: Nanjing University of Science & Technology, 2008: 16-19. (in Chinese)

[28] 魏海坤. 神经网络结构设计的理论与方法[M]. 北京:国防工业出版社, 2005: 8-11. Wei Haikun. Theory and method of neural network structure design[M]. Beijing: National Defense Industry Press, 2005: 8-11. (in Chinese)
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