Volume 40 Issue 1
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YANG Linfeng, MOU Rui, LI Xin, LI Wei. Development of an Object Tracking Algorithm for Airports Using Adaptive Filter Update Technique[J]. Journal of Transport Information and Safety, 2022, 40(1): 72-79. doi: 10.3963/j.jssn.1674-4861.2022.01.009
Citation: YANG Linfeng, MOU Rui, LI Xin, LI Wei. Development of an Object Tracking Algorithm for Airports Using Adaptive Filter Update Technique[J]. Journal of Transport Information and Safety, 2022, 40(1): 72-79. doi: 10.3963/j.jssn.1674-4861.2022.01.009
shu

Development of an Object Tracking Algorithm for Airports Using Adaptive Filter Update Technique

doi: 10.3963/j.jssn.1674-4861.2022.01.009
  • 1.

    Civil Aircraft Fire Science and Safety Engineering Key Laboratory of Sichuan Province, Civil Aviation Flight University of China, Guanghan 618307, Sichuan, China

  • 2.

    WisesoftCo., Ltd., Chengdu 610045, China

  • 3.

    College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, Sichuan, China

  • 4.

    Civil Aviation Flight University of China Guang Han College, Guanghan 618307, Sichuan, China

  • 5.

    College of Civil Aviation Safety Engineering, Civil Aviation Flight University of China, Guanghan 618307, Sichuan, China

  • 6.

    School of Aeronautics and Astronautics, Sichuan University, Chengdu 610065, China

  • Received Date: 2021-08-11
    Available Online: 2022-03-31
    Abstract
  • Tracking objects over airport surface is often hindered by the factors such as occlusion, background clutter and low resolution, which often result in reduced tracking accuracy or even loss of tracked objects. In order to mitigate the above problems, an object tracking algorithm for airports based on adaptive filter update is developed. First, the color and convolutional neural network feature of the tracking object are selected. Based on these features, multi-feature fusion is performed through an interpolation operator. Then, the fusion feature and its corresponding filter are convolved and summedin order to calculate the confidence level of each region.Theregion with a high confidence level is then identified as thelocation of the tracked object. By using the peak to side-lobe ratio and the average peak-to-correlation energy, a verification method is developed to improve the tracking accuracy. Furthermore, a self-adaptive updating algorithm is designed to adjust the learning rate of the filter and updated only when the results are reliable. According to the results obtained using a video dataset collected at an airport in Southwest China, the proposed algorithm has a better tracking performance when the object features change or are unclear, and the results also indicate the tracking performance is significantly improved under 9 different factors, such as occlusions and background clutter. The overall accuracy and success rate are 0.834 and 0.828 respectively, which are higher than that of the original ECO algorithm by 11.35% and 11.29%, and are superior to the other five classical algorithms.

     

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    • References(17)
  • [1]
    高策, 褚端峰, 何书贤, 等. 基于卡尔曼-高斯联合滤波的车辆位置跟踪[J]. 交通信息与安全, 2020, 38(1): 76-83. doi: 10.3963/j.jssn.1674-4861.2020.01.010

    GAO C, CHU D F, HE S X, et al. Vehicle position tracking based on joint kalman-gaussian filter[J]. Journal of Transport Information and Safety, 2020, 38(1): 76-83. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2020.01.010
    [2]
    梁海军, 张雪华, 夏正洪. 基于概率修正的机场场面运动航空器跟踪算法[J]. 科学技术与工程, 2017, 17(19): 84-91. doi: 10.3969/j.issn.1671-1815.2017.19.014

    LIANG H J, ZHANG X H, XIA Z H. Tracking algorithm of moving aircraft in airport based on probability correction[J]. Science Technology and Engineering, 2017, 17(19): 84-91. (in Chinese) doi: 10.3969/j.issn.1671-1815.2017.19.014
    [3]
    潘卫军, 罗杰, 王少杰, 等. 基于跑滑系统约束的航空器滑行跟踪算法[J]. 四川大学学报(自然科学版), 2019, 56(5): 843-850. doi: 10.3969/j.issn.0490-6756.2019.05.009

    PAN W J, LUO J, WANG S J, et al. Taxiing tracking algorithm of aircraft on the ground based on the runway-taxiway system constraints[J]. Journal of Sichuan University(Natural Science Edition), 2019, 56(5): 843-850. (in Chinese) doi: 10.3969/j.issn.0490-6756.2019.05.009
    [4]
    穆柯楠, 王会峰, 杨澜, 等. 基于多尺度边缘融合及SURF特征匹配的车辆检测及跟踪方法[J]. 交通信息与安全, 2018, 36(6): 65-73. doi: 10.3963/j.issn.1674-4861.2018.06.009

    MU K N, WANG H F, YANG L, et al. A method of detection and tracking vehicles based on multi-scale edge fusion and SURF feature matching[J]. Journal of Transport Information and Safety, 2018, 36(6): 65-73. (in Chinese) doi: 10.3963/j.issn.1674-4861.2018.06.009
    [5]
    孙博, 王阿川. 融合深度特征和FHOG特征的尺度自适应相关滤波跟踪算法[J]. 河北科技大学学报, 2021, 42(6): 591-600. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ202004036.htm

    SUN B, WANG A C. Scale-adaptive correlation filter tracking algorithm fusing depth features and FHOG features[J]. Journal of Hebei University of Science and Technology, 2021, 42 (06): 591-600. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ202004036.htm
    [6]
    孟晓燕, 段建民. 基于相关滤波的目标跟踪算法研究综述[J]. 北京工业大学学报, 2020, 46(12): 1393-1416. doi: 10.11936/bjutxb2019030011

    MENG X Y, DUAN J M. Advances in correlation filter-based object tracking algorithms: a review[J]. Journal of Beijing University of Technology, 2020, 46(12): 1393-1416. (in Chinese) doi: 10.11936/bjutxb2019030011
    [7]
    徐宁, 王娟娟, 郭晓雨, 等. 判别式相关滤波器的目标跟踪综述[J]. 小型微型计算机系统, 2020, 41(12): 2484-2493. doi: 10.3969/j.issn.1000-1220.2020.12.006

    XU N, WANG J J, GUO X Y, et al. Survey of visual tracking based on discriminative correlation filters[J]. Journal of Chinese Computer Systems, 2020, 41(12): 2484-2493. (in Chinese) doi: 10.3969/j.issn.1000-1220.2020.12.006
    [8]
    WANG M M, LIU Y, HUANG Z Y. Large margin object tracking with circulant feature maps[C]. IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA: IEEE, 2017.
    [9]
    谢维信, 赵田. 多特征自适应融合的相关滤波目标跟踪算法[J]. 信号处理, 2021, 37(4): 603-615. https://www.cnki.com.cn/Article/CJFDTOTAL-XXCN202104015.htm

    XIE W X, ZHAO T. Multi-feature adaptive fusion based target tracking algorithm[J]. Journal of Signal Processing, 2021, 37(4): 603-615. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XXCN202104015.htm
    [10]
    李健宁, 曹文君, 刘晓利. 结合多尺度改进颜色特征应对遮挡的跟踪算法[J]. 兵器装备工程学报, 2019, 40(6): 134-138+197. doi: 10.11809/bqzbgcxb2019.06.028

    LI J N, CAO W J, LIU X L. Tracking algorithm for target occlusion by improving color features combined with scale pyramid[J]. Journal of Ordnance Equipment Engineering, 2019, 40(6): 134-138+197. (in Chinese) doi: 10.11809/bqzbgcxb2019.06.028
    [11]
    王科平, 朱朋飞, 杨艺, 等. 多时空感知相关滤波融合的目标跟踪算法[J]. 计算机辅助设计与图形学学报, 2020, 32 (11): 1840-1852. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJF202011014.htm

    WANG K P, ZHU P F, YANG Y, et al. Target tracking algorithm based on multi-time-space perception correlation filters fusion[J]. Journal of Computer-Aided Design and Computer Graphics, 2020, 32(11): 1840-1852. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJF202011014.htm
    [12]
    毛宁, 杨德东, 杨福才, 等. 基于分层卷积特征的自适应目标跟踪[J]. 激光与光电子学进展, 2016, 53(12): 195-207. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ201612027.htm

    MAO N, YANG D D, YANG F C, et al. Adaptive object tracking based on hierarchical convolution features[J]. Laser and Optoelectronics Progress, 2016, 53(12): 195-207. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ201612027.htm
    [13]
    MA C, HUANG J B, YANG X K, et al. Hierarchical convolutional features for visual tracking[C]. IEEE International Conference on Computer Vision, Santiago, Chile: IEEE, 2015.
    [14]
    DANELLJAN M, BHAT G, KHAN F S, et al. ECO: Efficient convolution operators for tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA: IEEE, 2017.
    [15]
    BHAT G, JOHNANDER J, DANELLJAN M. Unveiling the power of deep tracking[C]. European Conference on Computer Vision, Munich, Germany: Springer, 2018.
    [16]
    李欣, 周婧琳, 厚佳琪, 等. 基于ECO-HC改进的运动目标跟踪方法研究[J]. 南京大学学报(自然科学), 2020, 56(2): 216-226. https://www.cnki.com.cn/Article/CJFDTOTAL-NJDZ202002007.htm

    LI X, ZHOU J L, HOU J Q, et al. Research on improved moving object tracking method based on ECO-HC[J]. Journal of Nanjing University(Natural Science), 2020, 56(2): 216-226. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-NJDZ202002007.htm
    [17]
    李国友, 张凤熙, 纪执安. 自适应多滤波器的高效卷积算子目标跟踪算法[J]. 光电工程, 2020, 47(7): 50-62. https://www.cnki.com.cn/Article/CJFDTOTAL-GDGC202007006.htm

    LI G Y, ZHANG F X, JI Z A. Adaptive multi-filter tracker based on efficient convolution operator[J]. Opto-Electronic Engineering, 2020, 47(7): 50-62. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GDGC202007006.htm
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