Object Tracking Method Based on Appearance Adaptive Spatial Regularized Correlation Filter

Abstract

The invention discloses a target tracking method based on an appearance self-adaptive space regularization correlation filter, comprising: using an online K-means clustering algorithm to segment a tracking result image block from a target and a background, and obtain a target area template; The target area template is used to generate a spatially regularized weight matrix; the alternating direction multiplier method (ADMM) is used to train and learn a correlation filter with spatial regularization to track the target. The invention can effectively limit the learning content of the correlation filter, reduce the background information of the correlation filter, and suppress the boundary effect of the correlation filter. Compared with the traditional correlation filter with spatial regularization, it can more accurately suppress the target area and the background area to varying degrees; it can expand the search range of the correlation filter and improve the robustness of the correlation filter to large target displacements .

Description

technical field [0001] The invention relates to an object tracking method based on an appearance adaptive space regularization correlation filter, and belongs to the technical field of video object tracking. Background technique [0002] Target tracking is of great significance to the development of robotics, unmanned aerial vehicle, automatic driving, navigation and guidance and other fields. For example, in the process of human-computer interaction, the camera continuously tracks the human body's behavior, and through a series of analysis and processing, the robot can understand the human body's posture, movement, and gestures, so as to better realize the friendly communication between humans and machines; During the target tracking process of the UAV, the visual information of the target is continuously obtained and transmitted to the ground control station, and the video image sequence is analyzed through an algorithm to obtain the real-time position information of the t...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved in the present invention is: although the standard spatial augmentation regularization correlation filter algorithm can solve the boundary effect well, its regularization strategy only implements different penalty coefficients to the filter according to the position of the coefficient of the filter, but This is not good for the filter to learn the target information, so it is necessary to find a regularization strategy that can suppress the boundary effect and better learn the target information

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