Visual tracking for underwater sea cucumber via correlation filters

Authors

  • Honglei Wei School of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian 116038, Liaoning, China
  • Xiangzhi Kong School of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian 116038, Liaoning, China
  • Xianyi Zhai School of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian 116038, Liaoning, China
  • Qiang Tong School of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian City, Liaoning Province,China
  • Guibing Pang School of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian City, Liaoning Province,China

Keywords:

visual tracking, correlation filters, kernelized correlation filters, sea cucumber, scale estimation, underwater

Abstract

One of the essential techniques for using underwater robots to fish sea cucumbers is that the robots must track sea cucumbers using computer vision technology. Tracking underwater targets is a challenging task due to suspension, water absorption, and light scattering. This study proposed a simple but effective algorithm for sea cucumber tracking based on Kernelized Correlation Filters (KCF) framework. This method tracked the head and tail of the sea cucumber respectively and calculated the scale change according to the distance between the head and tail. The KCF method was improved on three strategies. First of all, the target was searched at the predicted position to improve accuracy. Secondly, an adaptive learning rate updating method based on the detection score of each frame was proposed. Finally, the adaptive size of the histogram of the oriented gradient (HOG) feature was used to balance the accuracy and efficiency. Experimental results showed that the algorithm had good tracking performance. Keywords: visual tracking, correlation filters, kernelized correlation filters, sea cucumber, scale estimation, underwater DOI: 10.25165/j.ijabe.20231603.4503 Citation: Wei H L, Kong X Z, Zhai X Y, Tong Q, Pang G B. Visual tracking for underwater sea cucumber via correlation filters. Int J Agric & Biol Eng, 2023; 16(3): 16(3): 247–253.

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Published

2023-08-17

How to Cite

Wei, H., Kong, X., Zhai, X., Tong, Q., & Pang, G. (2023). Visual tracking for underwater sea cucumber via correlation filters. International Journal of Agricultural and Biological Engineering, 16(3), 247–253. Retrieved from https://ijabe.migration.pkpps06.publicknowledgeproject.org/index.php/ijabe/article/view/4503

Issue

Vol. 16 No. 3 (2023): IJABE

Section

Information Technology, Sensors and Control Systems

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