Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (6): 1620-1630.doi: 10.23940/ijpe.19.06.p13.16201630

Previous Articles     Next Articles

Anti-Occlusion Moving Target Tracking Method

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

  1. a College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an, 710054, China
    b School of Computer Engineering, Jiangsu University of Technology, Changzhou, 213001, China
    c College of Electric and Control Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China
  • Submitted on ;
  • Contact: * E-mail address: an6860@126.com
  • About author:Hongan Li received his Ph.D. in computer science from Northwest University, Xi'an, China in 2014. He is currently an associate professor at Xi'an University of Science and Technology, Xi'an, China. His research interests include image processing, computational photography, and intelligent information processing;Zhuoming Du received his Ph.D. in computer science from Northwest University, Xi'an, China in 2012. He is currently a postdoctoral researcher in the School of Mathematical Sciences at Nanjing Normal University, Nanjing, China. His research interests include computational photography, deep learning, and optimization theory; Zhanli Li received his Ph.D. from the School of Mechanical Engineering at Xi'an Jiaotong University, Xi'an, China in 1997. He is currently a professor in the College of Computer Science and Technology at Xi'an University of Science and Technology, Xi'an, China. His research interests include intelligent video processing and intelligent information processing;Shuai Hao received his Ph.D. from the School of Automation at Northwestern Polytechnical University, Xi'an, China in 2014. He is currently a postdoctoral researcher at Xi'an University of Science and Technology, Xi'an, China. His research interests include intelligent video processing and intelligent information processing.Jiaying Chen is a master's student in the College of Computer Science and Technology at Xi'an University of Science and Technology, Xi'an, China. Her research interest is video target tracking.
  • Supported by:
    This work is partially supported by the Natural Science Basic Research Plan in Shaanxi Province of China (2019JM-162, 2019JM-020) and the Doctoral Research Startup Foundation of Xi'an University of Science and Technology (2019QDJ007).

Abstract: 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.

Key words: moving target tracking, occlusion, wavelet transform, Camshift, distance-based Kalman filter