Sequential Cross-correlation Object Tracking Based on Siamese Network
Existing Siamese network object tracking techniques perform only one fusion operation of template features and search features,which makes the object features on the fused feature map relatively coarse and unfavorable to the tracker's precise positioning.In this study,a serial mutual correlation module is designed.It aims to use the existing mutual correlation method to enhance the object features on the fused feature map by performing multiple mutual correlation operations on the template features and the search features,so as to improve the accuracy of the subsequent classification and regression results and strike a balance between speed and accuracy with fewer parameters.The experimental results show that the proposed method achieves good results on four mainstream tracking datasets.
deep learningobject trackingvideo surveillanceSiamese network
NETL
NSTL
万方数据
