Improved kernel-related filtering tracking method based on ultra-pixel optical flow and self-adaptive learning factor

Abstract

The invention discloses an improved kernel-related filtering tracking method based on ultra-pixel optical flow and self-adaptive learning factor. The appearance reconstruction of target can be realized through ultra-pixel analysis, and the target is divided into ultra-pixel blocks which are clustered into an ultra-pixel center. The displacement change of the optical flow analysis pixel point of each ultra-pixel center is calculated, and the movement offset and scale change of the target can be detected. Based on the predicted parameter, cycled sampling is conducted on each new-frame image, andan improved and gauss kernel-based filtering target tracking method which introduces the self-adaptive learning factor is adopted by each sample, and the accurate position and scale of the target canbe detected. The detection result is detected and corrected through an on-line SVM detection model, and the position with low confidence is corrected, and finally the target position can be accurately positioned and the target accurate scale can be obtained. The invention is advantageous in that the tracking problems like scale change, shielding, deforming, and motion blur, which exit in the target tracking process can be overcome, and real-time and highly-precise target tracking can be realized.

Description

Technical field [0001] The invention relates to the technical field of image processing and analysis, in particular to a nuclear correlation filtering tracking method based on superpixel optical flow and an improved adaptive learning factor. Background technique [0002] The full integration of computer technology and artificial intelligence not only promotes the development of computer science, but also greatly facilitates people's daily life. As an important field that leads computers to intelligentization, computer vision technology has attracted widespread attention from the society. As a key component of computer vision, visual target tracking technology can be widely used in many occasions such as human-computer interaction, pedestrian and vehicle monitoring, and drone navigation. Because target tracking algorithms have extremely broad application prospects, in recent years, domestic and foreign researchers have proposed a large number of advanced target tracking algorithm...

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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