Target tracking method based on structured output correlation filter

Abstract

The invention discloses a target tracking method based on a structured output correlation filter. The method mainly solves the problem that the tracking is failed due to the change, the shading, the rotation and the like of the target illumination in the prior art. The method comprises the following steps of (1) preprocessing a first frame image; (2) constructing a structured output correlation filter; (3) figuring out an optimal structured output correlation filter; (4) preprocessing a current frame image; (5) determining the position of a to-be-tracked target in the current frame image; (6)optimizing the structured output correlation filter; (7) judging whether all frame images in a to-be-tracked video image sequence are selected or not; if yes, ending the process; otherwise, executingthe step (4). According to the invention, the information contained in a sample can be better described by constructing the structured output correlation filter. As a result, characteristics with highdistinguishing degree can be learned through the structured output correlation filter, and a target can be tracked stably and accurately.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a target tracking method based on a structured output correlation filter in the technical field of computer vision image processing. The invention adopts a method based on a structured output correlation filter to realize moving target tracking in the fields of video monitoring, medical care, intelligent transportation, robot navigation, human-computer interaction, virtual reality and the like. Background technique [0002] The main task of target tracking is to estimate the trajectory of the target in the video, that is, to detect the moving target to be tracked from the video image sequence, and then determine the position of the moving target in each frame of image. One of the most popular methods for object tracking is detection tracking, which is usually based on a discriminative learning model, where a binary classifier is learned online to separate the objec...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that correlation filters constructed by cyclically shifting only object regions in the image are particularly sensitive to biases in translation and thus do not generalize well to other types of appearance changes such as lighting, viewpoint , scale, rotation, etc., so that it cannot adapt to the target tracking when the target and background change in the actual scene

Since the finite boundary correlation filter only processes samples, but does not pay attention to the matching problem between samples and labels, the smooth Gaussian function of the traditional correlation filter is still used to generate labels while expanding the filter size, and the samples of the target are not included. Blocks are also given positive labels. Therefore, the disadvantage of this method is that the limited boundary correlation filter cannot learn highly discriminative features, making the robustness of target tracking not high.

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