Target tracking method and target tracking device

Abstract

The invention relates to the technical field of image processing, in particular to a target tracking method and a target tracking device. The method includes: acquiring a current frame image during target tracking, comparing the current frame image to a previous frame image to obtain a frame difference template; performing foreground detection by the current frame image and a pre-established background template to obtain a foreground template; fusing and processing the frame difference template and the foreground template to obtain a foreground image of a candidate area after fusion; and tracking a determined tracking target in the foreground image of the candidate area. Most background interference is eliminated from the foreground area of the extracted fused candidate target, so that the target can be tracked in the effective area, chances of getting trapped in local peaks during target tracking are slimmed, interference resistance of the target tracking method is further improved and algorithmic accuracy and robustness are improved.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a target tracking method and device. Background technique [0002] Object detection and tracking is the main research direction of dynamic image processing technology, which has a wide range of application prospects, especially in the fields of intelligent monitoring, human-computer interaction and auxiliary medical diagnosis. Through target tracking, the system can automatically obtain information such as the position and size of the tracking target in the video or image. In the general video target tracking method, a reference template that identifies the tracking target or the object to be detected is established in advance as the basis for identifying and determining the position of the tracking target, and then uses the reference template to compare with each sub-region in the real-time image to find The position of the sub-region image closest to the track...

AI Technical Summary

Problems solved by technology

[0004] In the process of realizing the present invention, the inventors found that there are at least the following problems in the prior art: in the prior art, although the mean shift algorithm uses the kernel color histogram to model the tracking target, when the tracking target is relatively weak, When the target color is close to the background color, the mean shift algorithm is easily disturbed by the background color, thus falling into the local extreme point, and then losing the tracking target. The robustness of the algorithm is not strong

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