Aiming at application of machine vision technology in embedded platform

a kernel correlation filter tracking model with occlusion detection and adaptive template updating is proposed to improve the robustness of scale change and occlusion when the tracker is running on embedded platforms. In order to cope with the change of target scale during the target movement

multi-scale estimation and dynamic template update strategy are added to the kernel correlation filter

At the same time

in order to ensure that the target can be detected again after occlusion

a particle filter is used to resample multiple particle candidates to detect the target. Experimental results show that the mean distance precision（ mean DP） and mean overlap rate precision（ mean OP） of the tracker for the OTB dataset are 86. 6 % and 83. 7 %

respectively

and the average speed on the embedded platform is14. 56 fps.