Video Target Tracking Based On Correlated Particle Filter And Deep Neural Network

Video target tracking has attracted more and more attention and has been a very active research direction.The key problem of video target tracking is how to effectively learn the time-varying appearance of the target,eliminate the background clutter and maintain the real-time response.In this paper,the target tracking algorithm based on correlation filtering and particle filtering is analyze-d,and the existing algorithm is improved by using its advantages.The main work is as follows:(1)Aiming at the problems of common correlation filtering video tracking algorithms that are difficult to deal with target deformation and target out of view,an algorithm based on multi-feature integration is proposed.Based on the particle filter,the algorithm integrates the correlation filter as the particle observation model into the particle filter framework.Then update the particle weights according to the peak value of the correlation filter response graphs,and finally calculate the weighted sum of the latest weights of the particles to determine the target position.In addition,the method of multi-feature integration is used to enhance the expression ability of the target and improve the robustness of the algorithm.(2)In view of the complexity of the tracking environment and the different information represented by the depth features of different convolutional layers,a video tracking algorithm based on multi-layer depth feature fusion is proposed.The algorithm is based on the particle filter framework,extracts the features of the four-layer convolutional neural network and adaptively learns each correlation filter to obtain the corresponding response graph,thereby reducing the sampling ambiguity.In addition,the peak value of the response graphs after fusion are used to update the weight of particles,and the target position is obtained according to the estimated state corresponding to the maximum weight of particle,so as to obtain more accurate target prediction.(3)Aiming at the problem that the video target tracking algorithm based on the siamese network is very rough in predicting the target position,combining the analysis of the correlation particle filter framework,on the basis of the particle filter,combining the siamese network with it,the video tracking algorithm based on the siamese particle is proposed.The maximum confidence response graphs obtained from the siamese network structure are used to update the weight of the particles,and finally the target position is determined according to the most powerful particle.The tracking algorithms proposed in this paper ensure the tracking accuracy in complex scenes and reduce the computational cost of particle filtering.By selecting OTB-100 data set for qualitative and quantitative experiments,it shows that the tracking algorithms proposed in this paper have better tracking performance compared with some existing tracking algorithms.