Fast correlation tracking method for real-time video target

Abstract

The invention relates to a fast correlation tracking method for a real-time video target. The method comprises the following steps: (1) selecting a tracking target in a frame image, and determining a template image; (2) compressing the template image and the current frame image according to a compression ratio factor to obtain a new template image M and a new current frame image; (3) determining a searching range in the current frame image; (4) calculating a correlated characteristic parameter Q of each subregion image and the template image in the searching range, and selecting the subregionimage with a maximum correlated characteristic parameter Qmax as the current tracking target; (5) when the correlated characteristic parameter is greater than a maximum threshold T1, updating the template image; and (6) when the correlated characteristic parameter is less than an intermediate threshold T2 and greater than a minimum threshold T3, adopting a variable resolution method to update thetemplate image. The fast correlation tracking method for the real-time video target has rational design, realizes real-time tracking of a large target and a large searching range, improves tracking precision and searching speed, ensures correct tracking when the target becomes larger gradually or becomes smaller gradually, and improves tracking effect.

Description

technical field [0001] The invention belongs to the field of video image processing, in particular to a fast correlation tracking method of a real-time video target. Background technique [0002] Video image tracking technology is an indispensable key technology in computer vision. It has been widely used in many aspects such as robot visual navigation, military visual guidance, safety monitoring, traffic control, medical diagnosis, video compression and meteorological analysis. For example, video target tracking technology has been successfully applied in weapon imaging guidance, military reconnaissance and surveillance; it is used in the security monitoring of communities and important facilities to ensure the safety of communities and important facilities; it is used in intelligent transportation systems to realize Real-time detection and tracking of running vehicles, while obtaining important traffic flow parameters such as traffic volume, vehicle type, vehicle speed, an...

AI Technical Summary

Problems solved by technology

The existing problems are: (1) It is necessary to compare the template image with each sub-region image in the real-time image, which requires a large amount of calculation. When the target is large or the search range is large, the processing speed is slow, which cannot meet the requirements of real-time tracking. (2) By establishing a template image as the basis for identifying and determining the target position, when the tracking target gradually becomes larger or smaller, the template image is not updated in time, and the template image and the tracking target no longer match, target loss or tracking errors will occur

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