基于二阶池化网络的鲁棒视觉跟踪算法

Robust Visual Tracking Based on Second Order Pooling Network

针对低分辨率、遮挡以及相似物体干扰等复杂场景下目标易丢失的问题,本文提出了基于二阶池化网络的视觉跟踪算法.已有的方法大多采用一阶池化网络,使得对低分辨目标和相似目标间的区分性不足.对此,本文首先在VGG16网络结构的基础上,将网络最后的一阶池化层替换为二阶协方差池化层,接着在ImageNet和CUB200-2011数据集上对网络进行重新训练.在跟踪阶段,为了减少运算负担,仅提取预训练网络的第四层卷积特征作为目标的外观表征.最后将提取的特征与已有的相关滤波算法进行结合.实验结果表明,本文算法在跟踪精度和成功率上均取得了优异的性能表现.

Aiming at the problem that the target is easy to lose in the complex scene such as low resolution,occlusion,the interference of similar objects,this paper proposes a visual tracking algorithm based on second-order pooling network.Most of the existing methods use the first-order pooling network,which makes the difference between similar targets insufficient.In this paper,based on the VGG16 network structure,the last first-order pooling layer is replaced by the second-order covariance pooling layer,and then the network is retrained on ImageNet and CUB200-2011 image data sets.In order to reduce the computational burden,only the fourth convolution feature of the pre-training network is extracted as the appearance representation of the target.Finally,the extracted features are combined with the existing correlation filtering algorithm.The experimental results show that the algorithm achieves excellent performance in tracking accuracy and success rate.