摘要
视觉目标跟踪是计算机视觉领域一个重要研究方向,在自动驾驶、视频监控、人机交互、医疗诊断等众多领域有着广泛的应用。随着深度学习的崛起,基于神经网络的视觉目标跟踪已成为主流研究方向,其中基于双胞胎(Siamese)网络模型的方法在目标跟踪领域表现出了优异的性能。本文将基于Siamese网络,探究不同干扰和网络结构对目标跟踪性能的影响。
Visual object tracking is an important research in computer vision field, with a range of applications such as autonomous driving, video surveillance, human-computer interaction, medical diagnosis, etc. With the rise of deep learning, neural networks have been employed in the mainstream frameworks for visual object tracking. Among them, the methods built on architecture of Siamese networks have shown excellent tracking performance. In this paper, we will investigate the effects of different interference and network structures on target tracking performance based on Siamese networks.
出处
《计算机科学与应用》
2021年第5期1468-1473,共6页
Computer Science and Application
