基于孪生网络的串联互相关目标跟踪
Sequential Cross-correlation Object Tracking Based on Siamese Network
陈凤姣 1程旭2
作者信息
- 1. 南京信息工程大学软件学院,南京 210044
- 2. 南京信息工程大学计算机学院、网络空间安全学院,南京 210044
- 折叠
摘要
针对现有孪生网络目标跟踪技术只对模板特征和搜索特征进行一次融合操作,使得融合特征图上的目标特征相对粗糙,不利于跟踪器精确跟踪定位的问题,本文设计了一个串联互相关模块,旨在利用现有的互相关方法,对模板特征和搜索特征做多次的互相关操作增强融合特征图上的目标特征,提升后续分类和回归结果的准确性,以更少的参数实现速度和精度之间的平衡.实验结果表明,所提出的方法在 4 个主流跟踪数据集上都取得了很好的结果.
Abstract
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.
关键词
深度学习/目标跟踪/视频监控/孪生网络Key words
deep learning/object tracking/video surveillance/Siamese network引用本文复制引用
出版年
2024
NETL
NSTL
万方数据