| Video object tracking technology has always been an important branch of computer vision and plays a vital role in many real-time vision applications.The target tracking algorithm based on correlation filter is widely used because of the good balance between calculation speed and accuracy.In recent years,with the application of convolution and attention mechanisms in the field of computer vision,many correlated filter trackers combining convolution and attention features have emerged.Although many scholars still use various feature extraction methods to improve the algorithm,the performance of the obtained algorithm model is still limited.Aiming at the problem of large amount of computation and parameter of feature extraction network in correlation filter tracking algorithm,this paper proposes a tracking algorithm model based on ECO algorithm,which uses lightweight convolution attention fusion network as feature extraction network.The main research contents of this paper are as follows:(1)In order to solve the problem of high parameters and calculations in the feature extraction network,a Res Net-ACmix Co T feature extraction network integrating convolution and attention features is proposed.First,based on Res Net network,the convolution module in Res Net network is replaced by convolution attention fusion module ACmix and local self-attention module Co T.Finally,a feature extraction network,namely Res Net-ACmix Co T network,is built.The experimental results show that the calculation amount of Res Net-ACmix Co T network is reduced by 0.47 Gbit and the parameter amount is reduced by 0.54 Mbit compared with Res Net network.(2)In order to solve the problem of high parameters and computation of feature extraction network in correlation filter tracking algorithm,an improved ECO target tracking algorithm RACECT based on convolution attention fusion network Res Net-ACmix Co T is proposed.First,the Res Net-ACmix Co T network is used as the feature extraction network to extract the shallow and deep features of the image,and the principal component analysis is used to reduce the dimension of the extracted features,then the compressed features and manual features are interpolated and converted to the Fourier domain,and then the convolution calculation is performed with the current filter to obtain the target position according to the maximum response value,Finally,the Gaussian mixture model is used to optimize the sample set and the conjugate gradient algorithm is used to optimize the loss function to train and update the filter.The algorithm proposed in this paper is compared with the classical correlation filtering target tracking algorithm on VOT2016 data set and OTB50 data set.The experimental results show that the algorithm proposed in this paper has better performance than the traditional correlation filtering tracking algorithm. |
