Optimal Design of Kernal Correlation Filtering Target Tracking Method for Mobile Robot Based on ROS

Aiming at the problem that the tracking precision of small mobile robot decreases or even fails due to the change of illumination or the fast motion of human body,a kernel correlation filter guided by motion model(MMKCF)is proposed.By building a feet motion model to predict the position of the feet in video tracking,the algorithm obtains the target detection area of the kernel correlation filter tracking algorithm,which effectively solves the drift of the tracking box.Based on four groups of human walking video,target tracking experiments are carried out,and tracking performance of five algorithms such as MMKCF and KCF are compared.The experimental results show that the average tracking precision of MMKCF algorithm is about 0.77 when the illumination changes and the target moves rapidly,which is much higher than the other four tracking algorithms.Finally,the MMKCF algorithm is applied to the target tracking of TurtleBot robot under the robot operating system(ROS),and the foot tracking of human body during fast motion is successfully completed,which proves that the proposed algorithm has strong robustness and real-time performance.