Object Tracking of Structured SVM Based on DIoU Loss and Smoothness Constraints
Object tracking based on structured support vector machine has been widely concerned because of its excellent per-formance.However,the existing methods have the problems of imprecise loss function and model drift.To solve these two pro-blems,firstly,a structured SVM model is proposed based on DIoU loss and smoothness constraints.Secondly,DIoU function and L2 norm of the difference between wt and wt-1 are used respectively as the loss functions and the smoothness constraints in the model.Thirdly,the algorithm for the proposed model is designed with the dual coordinate descent principle.Finally,a multi-scale object tracking method is implemented via the proposed structured SVM on the basis of DIoU loss and smoothness constraints.The proposed object tracking method is experimentally validated on the OTB100 and VOT-ST2021 datasets,and the experimental results show that the tracking success rate of the Scale-DCSSVM on the OTB100 is 1.1％higher than the DeepSRDCF,and the EAO on VOT-ST2021 is 1.2％higher than the E.T.Track.The proposed object tracking method has superior performance.
NETL
NSTL
万方数据
