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Detection and tracking of human track and field motion targets based on deep learning

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Abstract

The detection and tracking of human moving objects is an important part of visual analysis of human movement and one of the important fields of computer vision. When using the existing methods to detect and track human track and field targets, there are some problems such as low detection accuracy, large target positioning error and low detection and tracking efficiency. A method of target detection and tracking in human track and field based on deep learning is proposed. The background subtraction method based on adaptive hybrid gaussian background model is used to detect the target. The read video image is denoised and smoothed. The holes in the foreground region are removed by morphological filtering, and the connected region of binary image is analyzed. Get the number and area of the connected areas. Human body area ratio and length-width ratio are used to classify and identify human body so as to complete the detection of human track and field sports target. Based on the structure of deep learning, combining the detection results of deep learning and LK tracking, PN learning was used to modify the parameters of the superposition automatic coding machine, avoiding the detection errors in deep learning and realizing the tracking of human track and field targets. Experimental results show that this method has higher detection accuracy, higher target positioning accuracy and higher detection and tracking efficiency.

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References

  1. Chen TS, Chen JJ, Wu CH (2016) Distributed object tracking using moving trajectories in wireless sensor networks. Wirel Netw 22:1–23

    Article  Google Scholar 

  2. Choi B, Park S, Kim E (2016) A newborn track detection and state estimation algorithm using Bernoulli random finite sets. IEEE Trans Signal Process 64:2660–2674

    Article  MathSciNet  Google Scholar 

  3. Geng PL, Song JC, Liu XF et al (2017) A method of mine moving objects detecting and tracking based on RGB color space. Journal of Taiyuan University of Technology 48:963–968

    Google Scholar 

  4. Gu SH, Lu B, Ma ZH (2017) Target detection and tracking algorithm based on contour and ASIFT feature. Computer Simulation 34:266–271

    Google Scholar 

  5. Gunes A, Guldogan MB (2018) Joint underwater target detection and tracking with the Bernoulli filter using an acoustic vector sensor. Digital Signal Processing 48:246–258

    Article  MathSciNet  Google Scholar 

  6. Kwon J, Kim K, Cho K (2017) Multi-target tracking by enhancing the kernelised correlation filter-based tracker. Electron Lett 53:1358–1360

    Article  Google Scholar 

  7. Li L, Fan T, Zhen S et al (2016) Object tracking with double-dictionary appearance model. Opt Eng 55:103–106

    Google Scholar 

  8. Li F, Zhang R, Feng Y (2017) Fast pedestrian detection and dynamic tracking for intelligent vehicles within V2V cooperative environment. IET Image Process 11:833–840

    Article  Google Scholar 

  9. Liu X, Chen SW, Aditya S et al. 2018. Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion 26:156-158

  10. Ma Y, Chang Q, Hu MF (2017) Research on infrared human detection from complex backgrounds. Infrared Technology 39:1038–1044

    Google Scholar 

  11. Marvasti FS, Mosavi MR, Nasiri M (2018) Flying small target detection in IR images based on adaptive toggle operator. IET Comput Vis 12:527–534

    Article  Google Scholar 

  12. Park JD, Doherty JF (2017) Track detection of low observable targets using a motion model. IEEE Access 3:1408–1415

    Article  Google Scholar 

  13. Shi J, Zhang SL (2017) Across first-order peak target detection based on RSMH for HF radar. Journal of China Academy of Electronics and Information Technology 12:25–30

    Google Scholar 

  14. Suhr JK, Jung HG (2016) Automatic parking space detection and tracking for underground and indoor environments. IEEE Trans Ind Electron 63:5687–5698

    Article  Google Scholar 

  15. Thomas J, Loianno G (2017) Autonomous flight for detection, localization, and tracking of moving targets with a small quadrotor. IEEE Robotics & Automation Letters 99:1–1

    Google Scholar 

  16. Xia K-j, Yin H-s, Zhang Y-d (2019) Deep semantic segmentation of kidney and space-occupying lesion area based on SCNN and ResNet models combined with SIFT-flow algorithm. J Med Syst 43:2

    Article  Google Scholar 

  17. Xue YD (2016) Application of binocular vision technology in human-computer interaction sports. Automation & Instrumentation 26:189–190

    Google Scholar 

  18. Xue H, Yao L, Deng C et al (2016) Tracking people in RGBD videos using deep learning and motion clues. Neurocomputing 204:70–76

    Article  Google Scholar 

  19. Yi M, Chu Y (2016) Small moving target detection and tracking based on multi-view aerial video registration. Computer Engineering and Applications 52:27–31

    Google Scholar 

  20. Yuan Y, Lu Y, Qi W (2017). Tracking as a Whole: Multi-Target Tracking by Modeling Group Behavior With Sequential Detection. IEEE Transactions on Intelligent Transportation Systems PP:1-11

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Authors and Affiliations

  1. Hebei Medical University, Shijiazhuang, China

    Yafei Zhang, Man Zhang & Dongyuan Zhang

  2. Hebei University of Engineering, Handan, China

    Yongxia Cui

Authors
  1. Yafei Zhang
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  2. Man Zhang
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  3. Yongxia Cui
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  4. Dongyuan Zhang
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Correspondence to Man Zhang.

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Zhang, Y., Zhang, M., Cui, Y. et al. Detection and tracking of human track and field motion targets based on deep learning. Multimed Tools Appl 79, 9543–9563 (2020). https://doi.org/10.1007/s11042-019-08035-9

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  • DOI: https://doi.org/10.1007/s11042-019-08035-9

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