A Target Tracking Optimization Method Based on Correlation Filter

Abstract

The invention discloses a correlation filter-based target tracking optimization method. The method mainly comprises the steps of obtaining a video, wherein the video comprises T frames of pictures, each frame of the picture contains a target, the position of the target on the first frame of the picture is known, and the positions of the targets on the residual T-1 frames of the pictures are unknown; then calculating target search domain regions of the T frames of the pictures; obtaining HOG eigenvectors fHOG, 1 to fHOG, T of the target search domain regions of the first to Tth frames of the pictures and color feature matrixes of the target search domain regions of the first to Tth frames of the pictures, determining a correlation filter, and calculating a target center point qt of the tthframe of the picture; and obtaining a target center point qT of the Tth frame of the picture, and ending the tracking, thereby obtaining target center points q2 to qT of the second to Tth frames of the pictures and a target center point q1 of the first frame of the picture. The real-time tracking of each target in the T frames of the pictures is realized.

Description

technical field [0001] The invention belongs to the field of computer vision, in particular to a target tracking optimization method based on a correlation filter, which is suitable for practical application. Background technique [0002] Video target tracking is an important research topic in the field of computer vision. Its definition is: given the position frame of the target in the initial frame of the video sequence, the position frame of the target is automatically given in the next video sequence. The research on video target tracking is in the video It has a wide range of applications in monitoring, human-computer interaction, robotics and other fields. [0003] After more than 30 years of research and development, a large number of video object tracking algorithms have been proposed one after another. Early work mainly focused on the research of generative learning tracking methods, such as Lucas-Kanade algorithm, Kalman filtering algorithm and Mean-shift tracking...

AI Technical Summary

Problems solved by technology

[0004] However, the current correlation filtering algorithm still has some deficiencies, such as the target is easily lost when tracking fast objects and deformable objects; the size of the search domain is not accurate enough, and the target information is too high when the proportion of the target in the search domain is too high. Many, when the proportion of the target in the search domain is too low, there is too much background information

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