Aiming at the problem of low tracking accuracy and even tracking failure caused by fast motion or occlusion of human targets by small mobile robots

a foot motion model was established to predict the position information of feet

and the target detection region of kernel correlation filter (KCF) was obtained. In this paper

a motion model guided adaptive kernel correlation filtering algorithm is proposed by combining with the output response peak neighborhood correlation detection. Foot tracking experiments were carried out on seven groups of videos under different scenarios. The results show that the average tracking accuracy of the adaptive response KCF algorithm guided by the motion model is the highest

and the tracking precision rate of the algorithm reaches 86% in the case of short-term occlusion

which is significantly higher than that of the adaptive response KCF

BACF and SAMF algorithm. Finally

the proposed algorithm is applied to the target tracking test of a Turtlebot robot under the ROS (Robot Operating System)

which successfully overcomes the influence of occlusion on feet tracking

and verifies that the proposed algorithm has strong robustness and real-time performance.