Robust and fast visual tracking for a ball and plate control system: design, implementation and experimental verification | Multimedia Tools and Applications Skip to main content
Log in

Robust and fast visual tracking for a ball and plate control system: design, implementation and experimental verification

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The ball and plate System (BPS) is a two-dimensional electromechanical system with multiple variables, non-linearity and strong coupling. The BPS control problem is to hold the rolling ball in a specific position on the plate by adjusting the plate inclination. Ball tracking is therefore the fundamental step in BPS control, which can largely influence the control effectiveness and efficiency. The segmented path planning based on the sequential thinning algorithm is one popular tracking technology. However, it suffers from high dependence on the operating environment, complex operation and slow speed. This paper innovatively proposes a robust and fast visual tracking solution for BPS. A novel hardware structure has been designed. The sensing camera is rigidly connected to the plate, which avoids the coordinate transformation and thus reduces the complexity. In path recognition, a parallel thinning algorithm is used to improve the processing speed. Additionally, in path planning, a window searching algorithm combining the slope order matching method is proposed to establish the linked list that describes the movement path. A cascaded structure of the BPS tracking controller is also designed. Experiments have shown the effectiveness of the whole system, exhibiting shorter travelling time, smaller tracking errors as well as better stability compared to conventional systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Bardyn JJ et al (1984) Une architecture vlsi pour un operateur de filtrage median. Congtres Reconnaissance des Forms et Intelligence Artificielle (vol. 1, pp. 557-566), Paris, 25-27

  2. Bataineh B, Abdullah SNHS, Omar K (2011) An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows. Pattern Recogn Lett 32(14):1805–1813

    Article  Google Scholar 

  3. Chung KL, Lin HY (1995) Hough transform on reconfigurable meshes. Comput Vis Image Underst 61:278–284

    Article  Google Scholar 

  4. Colmenares SG, Moreno-Armendariz MA, Yu W, Rodriguez FO (2012) Modeling and Nonlinear PD regulation for Ball and Plate System. Conference: World Automation Congress (WAC)

  5. Dong YX (2014) Review of Otsu Segmentation Algorithm. Adv Mater Res 989-994:1959–1961

    Article  Google Scholar 

  6. Duda RO, Hart PE (1975) Use of the Hough transformation to detect lines and curves in pictures. Communications of the Association for Computing Machinery 18:120–122

    Article  Google Scholar 

  7. Durus M, Ercil A (2007) Robust vehicle detection algorithm. Signal processing and Communications Applications. SIU 207, IEEE 15th, l-4

  8. Fan J, Han M (2012) Nonliear model predictive control of ball-plate system based on gaussian particle swarm optimization. WCCI 2012 IEEE World Congress on Computational Intelligence, Brisbane

    Book  Google Scholar 

  9. Fan X, Zhang N, Teng S (2004) Trajectory planning and tracking of ball and plate system using hierarchical fuzzy control scheme. Fuzzy Sets Syst 144(2):297–312

    Article  MathSciNet  Google Scholar 

  10. Guan-zheng TAN, Xiong XU, Hong-feng XIAO (2005) Real-time and Accurate Hand Path Tracking and Joint Trajectory Planning for Industrial Robots. Journal of Central South University (Science and Technology) 36(1):102–107

    Article  Google Scholar 

  11. Han K-w, Tian Y-t, Kong Y-s, Zhang Y-h, Li J-s (2014) Adaptive Decoupled Sliding Mode Control for the Ball and Plate System. Journal of Jilin University (Engineering and Technology Edition) 44(3):718–725

    Google Scholar 

  12. Jain A, Gupta R (2015) Gaussian filter threshold modulation for filtering flat and texture area of an image. Conference Proceeding - 2015 International Conference on Advances in Computer Engineering and Applications, ICACEA, 22:760-763

  13. KaewTraKulPong P, Bowden R (2001) An improved adaptive background mixture model for realtime tracking with shadow detection. In Proc. 2nd European Workshop on Advanced Video Based Surveillance Systems, AVBS01. VIDEO BASED SURVEILLANCE SYSTEMS: Computer Vision and Distributed Processing, Kluwer Academic Publishers

  14. Kimme C, Ballard DH, Sklansky J (1972) Finding circles by an array of accumulators. Communications of the Association for Computing Machinery 15:11–15

    Article  MATH  Google Scholar 

  15. Lam L, Lee S-W, Suen CY (1992) Thinning Methodologies-A Comprehensive Survey. IEEE Trans Pattern Anal Mach Intell 14(9):869–885

    Article  Google Scholar 

  16. Lee T-C, Kashyap RL, Chu C-N (1994) Building Skeleton Models Via 3-D Medial Surface Axis Thinning Algorithms. Computer Vision, Graphics, and Image Processing 56(6):462–478

    Google Scholar 

  17. Li Y, Wang Q, Lin Q, Deng N (2012) On Machine Vision based Ball and Plate Labyrinth System. Proceedings of the 31st Chinese Control Conference, Hefei

  18. Li LZ et al (2014) A New Approach for Gray Image Segmentation Using Level Set Method. Appl Mech Mater 530-531:372–376

    Article  Google Scholar 

  19. Ma JY, Jie FR, Hu YJ (2017) Moving target detection method based on improved Gaussian mixture model. Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 1042014. https://doi.org/10.1117/12.2282506

  20. Ma Y, Zhu W, Shixia A et al (2007) Improved moving target detection method based on Gaussian Mixture Model. Computer Application 27(10):2544–2548

    Google Scholar 

  21. Peng P, Tao Z (2017) Application of an Improved Background Algorithm in SIFT. Journal of Longyan University 35(2)

  22. Sang J, Fang Q, Xu C (2017) Exploiting Social-Mobile Information for Location Visualization. ACM Transactions on Intelligent Systems and Technology (TIST) - Special Issue: Mobile Social Multimedia Analytics in the Big Data Era and Regular Papers, 8(3)

  23. Sang J, Xu C (2012) Right buddy makes the difference: an early exploration of social relation analysis in multimedia applications. ACM Multimedia, pp. 18-28

  24. Sang J, Xu C, Liu J (2012) User-Aware Image Tag Refinement via Ternary Semantic Analysis. IEEE Transactions on Multimedia 14(3):883–895

    Article  Google Scholar 

  25. Stocker AA (2002) An improved 2D optical flow sensor for motion segmention. Circuits and Systems. ISCSA 2002. IEEE International Symposium on Volume 2, 26-29. Page(s):332-335

  26. Su X, Sun Z-s, Zhao S-m (2006) Fuzzy Control Method for Ball and Plate System. Computer Simulation 23(9):165–167

    Google Scholar 

  27. Wang G, Gai Q, Yu H, Wen X, Ren T (2014) Video target detection algorithms based on background subtraction. Journal of Engineering of Heilongjiang University 5(4):64–68

    Google Scholar 

  28. Wang Y, Sun M, Wang Z, Liu Z, Chen Z (2014) A novel disturbance-observer based friction compensation scheme for ball and plate system. ISA Trans 53:671–678

    Article  Google Scholar 

  29. Wang L, Xu L et al (2017) Straw Coverage Detection Method Based on Sauvola and Otsu Segmentation Algorithm. Agric Eng 17(4):29–35

    Google Scholar 

  30. Yuangang L, Qingsheng G, Yageng S, Lin Q, Zheng C (2015) An Algorithm for Skeleton Extraction Between Map Objects. Geomatics and Information Science of Wuhan University 40(2):264–268

    Google Scholar 

  31. Zhang TY, Suen CY (1984) A fast parallel algorithm for thinning digital patterns. Commun ACM 27(3):236–239

    Article  Google Scholar 

  32. Zhao Y-H, Shao H-X (2011) Research of ball and plate control system based on vision. Industry Control and Applications 30(10):12–15

    Google Scholar 

  33. Zhu S (2011) Edge detection based on mathematical morphology and image fusion. Proceedings of 2011 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference, CSQRWC 2011, v 2, p 1290-1293

  34. Zhu S, Giacobazzi R (2008) Hiding information in completeness holes: new perspectives in code obfuscation and watermarking. Software Engineering and Formal Methods 2008. SEFM '08. Sixth IEEE International Conference on, pp. 7-18

  35. Zou J-c, Zheng W-q, Yang Z-h (2017) A novel enhancement method for low illumination images based on microarray camera. Applied Mathematics-A Journal of Chinese Universities 32:313 ISSN 1005-1031

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lei Zheng.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, L., Hu, R. Robust and fast visual tracking for a ball and plate control system: design, implementation and experimental verification. Multimed Tools Appl 78, 13279–13295 (2019). https://doi.org/10.1007/s11042-018-6430-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6430-6

Keywords