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.