Extended target tracking method based on combination of skeleton characteristic points and distribution field descriptors

Abstract

The invention provides an extended target tracking method based on combination of skeleton characteristic points and distribution field descriptors. The extended target tracking method comprises the following steps: firstly, preprocessing images to be processed by using Gaussian smooth filtering to remove influence of noise on a subsequent algorithm; and subsequently, cutting the smoothed images by using a fuzzy C-means clustering algorithm FCM to obtain binary images, carrying out Hough transform on the cut and obtained binary images to detect a part with an obvious linear characteristic on the target, extracting characteristic points from a part without obvious linear characteristic on the target by using a skeleton, then carrying out linear fitting on the characteristic points on the skeleton to obtain a target axis, taking a intersection point of the straight lines of the obtained axis as an initial track point, then calculating a distribution field in a certain neighborhood field of the point and searching a best position in the neighborhood field when a next frame comes, so as to obtain the current best track point. Through the extended target tracking method, the fuzziness problem is solved; and the extended target tracking method is capable of stably tracking the extended target under a condition with a great posture change.

Description

technical field [0001] The invention relates to a method for tracking a maneuvering extended target, in particular to a method for tracking a maneuvering extended target by using Hough transformation, extracting feature points and distribution fields from a skeleton, and is mainly used in image processing and computer vision. The invention belongs to the technical field of target detection and tracking in a photoelectric measurement system. Background technique [0002] In the photoelectric measurement system, in order to improve the tracking accuracy, the field of view of the detector is relatively small, and the target size is too large. In the detector, therefore, the object takes on an expanded form. For long-distance target imaging, due to degrading factors such as atmospheric turbulence, system jitter, and optical system aberrations, the imaging of the target in the system is very blurred and the contrast is poor; in addition, the target has no texture information, ha...

AI Technical Summary

Problems solved by technology

Since the target has no texture and significant features, the attitude changes greatly, and the traditional tracking method based on gray features tends to lose the target when the target has a large attitude change, which cannot meet the requirements of practical applications. Therefore, it is urgent to study new methods. To meet the requirements of tracking engineering applications

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