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Research of Siamese Network based Object Tracking Algorithm using Variational Bayes feature point matching

In recent years, the tracking algorithm of Siamese network has shown great advantages. Under the premise of ensuring real-time and accuracy, this paper proposes an object tracking algorithm based on Variational Bayes feature point matching for Siamese network. The algorithm framework is divided into three branches, the Siamese network extracts the feature point branch, the mask generates the object contour point branch and the classification branch. In this paper, the extracted feature points are combined with the object contour points for Variational Bayesian point set matching, and the template image is updated according to the matching results, so as to solve the problem of tracking object loss caused by object contour change and half occlusion. The effectiveness of the proposed algorithm is verified by evaluating and comparing the public datasets VOT and OTB.