Research on visual object tracking algorithm based on improved twin network

Abstract

The tracking accuracy of traditional algorithm decreases when the target is in a complex environment or the target itself changes, and an improved correlation filtering twin network algorithm combining the attention mechanism is proposed. Fast wavelet is used for filtering and multi-scale feature signal extraction, and loss function is used to minimize the squared error between sample and label. Then correlation filtering algorithm is used to calculate the similarity of image between target template and candidate region. Attention mechanism is introduced to improve correlation filtering twin network structure, and the GloU’s online learning is advanced as an aid to determine template updating. The experimental results show that the algorithm have certain improvement effect for target shade, quick movement and deformation problems, so performance and accuracy of target tracking, the algorithm has better competitiveness.