A Real-time Underwater Robotic Visual Tracking Strategy Based on Image Restoration and Kernelized Correlation Filters
作者单位：University of Chinese Academy of Sciences Key Laboratory of Management and Control for Complex SystemsInstitute of Automation Chinese Academy of Sciences School of Mechanical Engineering and Automation Beihang university
会议名称：《第30届中国控制与决策会议》
会议日期：2018年
学科分类：080202[工学-机械电子工程] 08[工学] 080203[工学-机械设计及理论] 0804[工学-仪器科学与技术] 0802[工学-机械工程]
基 金：supported in part by the National Natural Science Foundation of China(61633017,61633004,61603388,61725305) in part by the Key Research and Development and Transformation Project of Qinghai Province(2017-GX-103) in part by the Key Project of Frontier Science Research of Chinese Academy of Sciences(QYZDJ-SSWJSC004)
关 键 词：Underwater robotic vision real-time underwater tracking underwater image restoration KCF
摘 要：In this paper, a real-time underwater robotic visual tracking strategy（RUTS） based on underwater image restoration and Kernelized Correlation Filters（KCF） is developed for underwater robots. A real-time and unsupervised advancement scheme（RUAS）, which is utilized in this strategy, performs robustly in restoring underwater images. The KCF, as a high-speed and accurate tracking method on land, is employed in this strategy. To handle the conflict between tracking speed and accuracy, we propose a tracking strategy based on KCF in video sequence restored by RUAS,comparing Histogram of Oriented Gradient（HOG） descriptors and raw pixels gray（RPG） descriptors. We define an index Ac to describe the tracking accuracy and regard the number of frames per second as computing speed. Results of contrast experiments show that the RPG, a much simpler descriptor, can achieve tracking accuracy as precise as HOG,accompanied by an increase of tracking speed up to 36%. Finally, experiments of the KCF-based tracker with RPG on different underwater objects demonstrate the feasibility of the formed RUTS.