基于VGG网络的鲁棒目标跟踪算法

针对传统目标跟踪算法中当目标被遮挡和受光照强度变化等多种因素干扰时，相关滤波器模板更新不准确，误差逐帧累积最终导致目标跟踪失败，提出了一种基于VGG网络的鲁棒目标跟踪算法。首先通过VGG网络对第1帧输入图像中的局部上下文区域提取平均特征图来建立相关滤波器模板；然后通过VGG网络对后续帧输入图像中的局部上下文区域提取平均特征图和仿射变换平均特征图；其次与核相关滤波跟踪算法相结合，自适应确定目标位置和最终目标位置；最后自适应更新最终平均特征图和最终相关滤波器模板。实验结果表明，本文算法在目标被遮挡和受光照强度变化等多种因素干扰时，仍具有较高的目标跟踪精度和较强的鲁棒性。

A robust target tracking algorithm based on VGG network

In the traditional target tracking algorithm, when the target is disturbed by various factors such as occlusion and light intensity changes, the correlation filter template updates incorrectly and the error accumulates frame by frame, eventually causing the target tracking failure. Therefore, this paper proposes a robust object tracking algorithm based on VGG network. Firstly, the VGG network is used to extract the average feature map of the local context area image to establish a correlation filter template in the first frame of the input image. Secondly, the VGG network is used to extract the average feature map and the affine transformation average feature map of the local context area image in the subsequent frame of the input image. Thirdly, combining the kernel correlation filter tracking algorithm, the target position and the final target position are adaptively determined. Finally, the algorithm adaptively updates the final average feature map and the final correlation filter template in the current frame of the input image. Experimental results show that the proposed algorithm still has high target tracking accuracy and robustness when the target is disturbed by various factors such as occlusion and light intensity changes.