A Local Anti-Joint Sparse Representation Target Tracking Method Based on Multi-Template Spatio-temporal Correlation

Abstract

A local anti-union sparse target tracking method based on multi-template spatio-temporal correlation belongs to the field of machine vision and pattern recognition, and aims to mine spatio-temporal correlation information between different templates, solve the problem of target tracking in complex scenes, and reduce computational complexity. Under the particle filter framework, collect candidate targets and divide local image blocks to construct an over-complete dictionary; initialize and update the sample set of target templates, and label each template according to the acquired frame number; establish a time series weight matrix as Guide the timing prior information of sparse representation; use the dictionary to perform weighted joint sparse representation of all samples in the target template set, and replace the joint sparse constraint item in the objective function with a parameter-containing Gaussian function to solve the optimization problem, and obtain the sparse representation coefficient matrix ;According to the row continuity and weight value of the non-zero element distribution in the coefficient matrix, the importance score of the candidate target is obtained, so as to achieve stable target tracking.

Description

technical field [0001] The invention relates to the fields of machine vision and pattern recognition, in particular to a multi-template spatio-temporal correlation local anti-union sparse representation target tracking method. Background technique [0002] Object tracking is an important research direction in the field of computer vision, and it has been widely used in public transportation intelligent monitoring systems, security systems, human-computer interaction and other fields. Due to the complexity of the tracking scene, the target appearance model often undergoes large and unpredictable changes due to the interference of external objects and its own deformation, which makes it a huge challenge to achieve continuous, stable and robust target tracking. In order to solve the above problems, it is necessary to establish an appropriate representation model for the target to be tracked to overcome complex situations such as illumination changes, scale changes, and local oc...

AI Technical Summary

Problems solved by technology

[0007] Based on the above technical problems, the present invention provides a local anti-joint sparse representation target tracking method based on multi-template spatio-temporal correlation, which aims to mine the spatio-temporal correlation information between different templates, so as to solve complex problems such as local occlusion, illumination changes, and scale changes. The tracking problem in the scene, while reducing the computational complexity

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