Username   Password       Forgot your password?  Forgot your username? 


Anti-Occlusion Moving Target Tracking Method

Volume 15, Number 6, June 2019, pp. 1620-1630
DOI: 10.23940/ijpe.19.06.p13.16201630

Hongan Lia, Zhuoming Dub, Zhanli Lia, Shuai Haoc, and Jiaying Chena

aCollege of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an, 710054, China
bSchool of Computer Engineering, Jiangsu University of Technology, Changzhou, 213001, China
cCollege of Electric and Control Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China


(Submitted on March 20, 2019; Revised on April 10, 2019; Accepted on June 2, 2019)


In the artificial intelligence field, using computer vision to track an object is an important research topic. Especially when the target reappears after being occluded for a while, it is hard to precisely track the moving target again. Therefore, this paper proposes an anti-occlusion target tracking strategy that can overcome the occluded problem. Firstly, to make the target clearer, we design a moving target detection method using the Gaussian mixture background subtraction method based on the wavelet transform, which removes the high-frequency noise of video images. Then, in the tracking process, altered strategies are taken to cope with different occlusion situations, which include three cases: no occlusion, partial occlusion, and severe occlusion. For the first two cases, we use the distance-based Kalman filter method to track the moving target. For the third case, we designed a method that combines the Camshift method with the distance-based Kalman filter method to track moving targets, which is more efficient than only using the distance-based Kalman filter method. According to one of the cases, our program automatically selects the corresponding method. Experimental results show that our strategy can track moving targets accurately whether targets are in occlusion situation or not.


References: 23

  1. H. A. Li, Z. M. Du, Z. L. Li, and B. S. Kang, “Research on Aerial Target Recognition and Tracking based on Grey and Textural Features,” Journal of Graphics, Vol. 37, No. 2, pp. 224-229, 2016
  2. H. Wei and H. Q. Guo, “A Target Tracking Algorithm for Medical Images via Sparse Decomposition and Support Vector Machine,” Research & Exploration in Laboratory, 2013
  3. M. Kristan, J. Matas, A. Leonardis, M. Felsberg, L. Cehovin, G. Fernandez, et al., “The Visual Object Tracking Vot2015 Challenge Results,” in Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 1-23, 2015
  4. F. L. Chang, L. Ma, and Y. Z. Qiao, “Target Tracking under Occlusion based on Feature Correlation Matching,” Journal of Image and Graphics, Vol. 11, No. 3, pp. 877-882, 2006
  5. X. Yang, J. Liu, P. Y. Zhou, and D. Zhou, “Adaptive Particle Filter for Object Tracking based on Fusing Multiple Features,” Journal of Jilin University (Engineering and Technology Edition), Vol. 45, No. 2, pp. 533-539, 2015
  6. H. R. Jia, “Research of Target Tracking Algorithm under Occlusion based on Feature Correlation Matching,” Northeast Normal University, 2009
  7. R. E. Kalman, “A New Approach to Linear Filtering and Prediction Problems,” Journal of Basic Engineering, pp. 35-45, 1960
  8. D. Comaniciu, V. Ramesh, and P. Meer, “Real-Time Tracking of Non-Rigid Objects using Mean Shift,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Vol. 2, IEEE, 2000
  9. T. Li, “Research on Video Segmentation under Occlusion,” Jiangnan University, 2012
  10. J. Wang, “Visual Tracking based on Sub-Space Motion Model,” Shangdong University, 2013
  11. B. Xu, D. Kai, and T. Hao, “Object Tracking based on Improved Mean Shift and SURF,” Computer Engineering and Applications, Vol. 49, No. 21, pp. 133-137, 2013
  12. Q. C. Dai, X. Y. Feng, and J. Liu, “Tracking Method for Object of Partial Occlusion based on Combination of Blob Modelling and Mean-Shif,” Computer Engineering and Applications, Vol. 18, No. 47, pp. 183-185, 2011
  13. W. S. Yu, X. H. Tian, Z. Q. Hou, and Y. F. Zha, “Online Visual Tracking based on Local Patch Learning,” Acta Electronica Sinica, Vol. 43, No. 1, pp. 74-78, 2015
  14. Z. Zhou, “Research on Anti-Occluding Target Tracking Algorithm in Complex Background,” Aero Weaponry, No. 6, pp. 36-39, 2007
  15. Y. Liu, Y. F. Zhang, and Y. F. Dong, “Anti-Occlusion Algorithm of Tracking Moving Object in Clutter Background,” Chinese Journal of Liquid Crysls and Displays, Vol. 25, No. 6, pp. 890-895, 2010
  16. T. W. Xia and X. Hou, “Robot Moving Target Tracking Algorithm based on Adaptive Kalman Filter,” Computer Measurement & Control, Vol. 23, No. 1, pp. 173-175, 2015
  17. W. J. Liu and Y. J. Zhang, “Edge-Color-Histogram and Kalman Filter-based Real-Time Object Tracking,” Journal of Tsinghua University (Science and Technology), Vol. 48, No. 7, pp. 1104-1107, 2008
  18. X. F. Wang, “Vehicle Tracking Algorithm under Occlusion in Video,” Shandong Normal University, 2013
  19. G. R. Bradski, “Real Time Face and Object Tracking as a Component of a Perceptual User Interface,” in Proceedings of the Fourth IEEE Workshop on Applications of Computer Vision, pp. 214-219, 1998
  20. W. Wang and Z. H. Meng, “An Object Tracking Algorithm based on Improved Camshift,” Information Technology, Vol. 1, No. 23, pp. 85-88, 2015
  21. J. X. Huang, W. L. Wu, C. J. Long, and M. J. Zhang, “Study of Moving Object Detection in Video and Its Application,” Computer Technology and Development, Vol. 24, No. 3, pp. 15-18, 2014
  22. Q. M. Shi, “Research of Object Tracking based on Camshift,” Anhui University, 2013
  23. P. Vasilis and A. Argyros, “Multiple Objects Tracking in the Presence of Long-Term Occlusions,” Computer Vision & Image Understanding, Vol. 114, No. 7, pp. 835-846, 2010


Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

This site uses encryption for transmitting your passwords.