An Improved Kernel Correlation Filter Tracking Method Based on Superpixel Optical Flow and Adaptive Learning Factor

Abstract

The invention discloses an improved kernel correlation filter tracking method based on superpixel optical flow and self-adaptive learning factors. The appearance reconstruction of the target is realized through the strategy of superpixel analysis, and the target is divided into superpixel blocks and clustered into superpixels. Center, calculate the optical flow of each superpixel center to analyze the displacement changes of pixels, and predict the motion offset and scale change of the target; based on the predicted parameters, after circular sampling in a new frame image, each sample Both adopt the Gaussian kernel-based correlation filter target tracking method improved by introducing adaptive learning factors to detect the accurate position and scale of the target; finally, the detection results are detected and corrected through the online double SVM detection model, and the position with low confidence Correction is carried out, and finally the position of the target is precisely positioned and the exact scale of the target is obtained. The invention overcomes the tracking problems such as scale change, occlusion, deformation, and motion blur existing in the target tracking process, and realizes real-time high-precision target tracking.

Description

technical field [0001] The invention relates to the technical field of image processing and analysis, in particular to an improved kernel correlation filter tracking method based on superpixel optical flow and adaptive learning factors. Background technique [0002] The full combination of computer technology and artificial intelligence not only promotes the development of the field of computer science, but also greatly facilitates people's daily life. As an important field leading computers to become more intelligent, computer vision technology has attracted widespread attention from the society. As a key component of computer vision, visual object tracking technology can be widely used in many occasions such as human-computer interaction, pedestrian and vehicle monitoring, and drone navigation. Due to the extremely broad application prospects of target tracking algorithms, researchers at home and abroad have proposed a large number of advanced target tracking algorithms i...

AI Technical Summary

Problems solved by technology

[0005] Although the target tracking technology has developed rapidly, there are still many challenging problems in the current target tracking process, such as environmental lighting changes, occlusion, deformation, motion blur and rotation, etc. The above tracking problems are still to be overcome by the target tracking algorithm difficulty

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