基于孪生网络的目标跟踪算法综述

A survey of target tracking algorithms based on Siamese network

孪生网络是由2个或多个人工神经网络建立的耦合框架,因其将回归问题转换为相似度匹配问题,备受计算机视觉领域的研究人员关注。随着深度学习理论的快速发展,目标跟踪技术在生活中得到了广泛的应用。基于孪生网络的目标跟踪算法以其相对优越的准确率和实时性逐渐代替了传统的目标跟踪算法,成为目标跟踪的主流算法。首先,介绍了目标跟踪任务面对的挑战和传统方法;然后,介绍了孪生网络的基础结构及其发展,汇总了近年来基于孪生网络的目标跟踪算法与相应设计原理;另外,介绍多个用于目标跟踪测试的主流数据集,并基于这些数据集对比了基于孪生网络的目标跟踪算法的性能;最后,提出基于孪生网络目标跟踪算法目前存在的问题及对未来的展望。

Siamese network is a coupled framework established by two or more artificial neural networks,which turns the regression problem into a similarity matching problem and has attracted much attention from researchers in the computer vision field.With the rapid development of deep learning theory,target tracking technology has been widely used in daily life.Siamese network-based target tracking algorithms have gradually replaced traditional target tracking algorithms with their relatively superior accuracy and real-time performance,becoming the mainstream algorithm for target tracking.Firstly,the challenges and traditional methods faced by target tracking tasks are introduced.Then,the basic structure and development of Siamese network are introduced,and the design principles of Siamese network-based target tracking algorithms in recent years are summarized.In addition,the performance of Siamese network-based target tracking algorithms is compared using multiple mainstream datasets for target tracking testing.Finally,the problems and prospects of Siamese network-based target tracking algorithms are proposed.