Target tracking method based on multi-characteristic adaptive fusion and kernelized correlation filtering technology

Abstract

The invention provides a target tracking method based on multi-characteristic adaptive fusion and kernelized correlation filtering technology. The method comprises steps of according to target position and the dimension of the previous frame tracking, acquiring a candidate region of target motion; extracting histogram characteristics and color characteristics in the gradient direction of the candidate region, fusing the two kinds of characteristics, carrying out Fourier transform so as to obtain a characteristic spectrum and then calculating kernelized correlation; determining the position and the dimension of the target at the current frame, and acquiring a target region; extracting histogram characteristics and color characteristics in the gradient direction of the target region, fusing the two kinds of characteristics, carrying out Fourier transform so as to obtain a characteristic spectrum and then calculating kernelized self-correlation; designing the adaptive target correlation and training a position filter model and a dimension filter model; and using a linear interpolation method to update the characteristic spectrums and the related filters. According to the invention, the discrimination capability of the models is improved; robustness of the target tracking of the target in a complex scene and the appearance change is improved; calculation complexity is reduced; and tracking timeliness is improved.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a target tracking method based on multi-feature adaptive fusion and kernel correlation filtering technology. Background technique [0002] Target tracking is an important research content in the field of computer vision. Target tracking is mainly based on the position of the target in the first frame or the first few frames of the video to estimate the trajectory of the target in the subsequent sequence. At present, there are two main categories of target tracking technologies: [0003] (1) Generative method: This method mainly uses the generative model to describe the appearance characteristics of the target, and finds the most similar target appearance in the subsequent sequence, that is, minimizes the reconstruction error by searching for candidate targets. More representative algorithms include sparse coding, online density estimation and principal component analysis (PCA). T...

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Problems solved by technology

[0005] However, the traditional discriminative methods have an important flaw, that is, in order to enhance the discriminative ability, a large number of training samples are often required, which also increases the computational burden, making these discriminative methods struggle in real-time tracking

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