Abstract: The traditional video target tracking methods usually prossess low accuracy.This paper proposed an improved scheme based on convolution neural network and the interactive likelihood algorithm,and optimized the particle filter algorithm on the basis of deep learning.To address the issue of deficient nonlinear fitting ability of the principal component analysis (PCA),a kernel principal component analysis (KPCA) tracking algorithm was provided to obtain the deeper characteristic expression of the target.Then,a novel interactive likelihood (ILH) method was performed for image-based trackers,which can non-iteratively compute the sampling of areas belonging to different targets and thus reducing the requirement for data associations.The performance of the presented algorithm was evaluated in comparison with several related algorithms on image datasets.The experimental results demonstrate the great robustness and accuracy of the proposed algorithm.

Key words: Convolutional neural network(CNN), Deep learning, Interactive likelihood(IL), Kernel principal component analysis(KPCA), Target tracking