Application of SVM in Object Tracking Based on Laplacian Kernel Function
Yaohui Wang, Jiyang Zhang · 2016
In the process of object tracking, because of the light, the angle of view as well as the target shape change causes the goal which is easy to lose. In this paper, we use the SVM based on Laplacian Kernel Function. Under the framework of Struck, it is applied to the object tracking, and comparing with other kernel functions, such as Linear Kernel Function, Gauss Kernel Function and so on, which can achieve the object tracking with high accuracy and stability.