Volume 40 Issue 1
Feb.  2022
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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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