Scale self-adaptive sea surface target tracking method based on edge detection

Abstract

The invention belongs to the technical field of computer vision, and particularly discloses a scale self-adaptive sea surface target tracking method based on edge detection. According to the method, an HED edge detection network is adopted to process the edge detection candidate area of the target, and the HED can detect the closed outer contour of the target without depending on the information before the current frame, so that the scale of the target is determined, the position of the target can be further corrected, and the target tracking precision is improved. Therefore, the method is a robust tracking algorithm, obtains good effects in different tracking scenes, solves the problem that the existing KCF only tracks the position of the target but does not estimate the scale of the target, and cannot process the large change of the scale of the sea surface target, and further improves the sea surface target tracking precision.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a scale-adaptive sea surface target tracking method based on edge detection. Background technique [0002] Object tracking refers to the location of an object in the first frame of a given video, and localizes that object in each subsequent frame. Sea surface target tracking is of great significance to the behavior analysis of sea surface targets and the navigation of marine systems such as unmanned boats and unmanned ships. Sea surface target tracking has difficulties such as large target scale changes, severe target jitter, and large changes in appearance when the target rotates out of the plane. Although the target tracking algorithm has been developed rapidly under the continuous research of scholars at home and abroad in recent years, it still cannot achieve good results in these situations. [0003] In recent years, scholars at home and abroad have prop...

AI Technical Summary

Problems solved by technology

In 2017, IBCCF trained four boundary filters to estimate the upper, lower, left, and right boundaries of the target, so as to determine the scale change of the target, and used the VGG deep neural network to extract the features of the central candidate area and the boundary candidate area, which further improved the accuracy of the tracker. However, IBCCF trains 4 filters and uses neural network to extract features, resulting in a tracking speed of only 1.25fps, which cannot achieve the purpose of real-time tracking

Although DSST, SAMF, KCFDP and IBCCF can adapt to the change of the target scale, for the situation that the appearance of the target often changes due to out-of-plane rotation and the scale changes are large, these trackers can only frame a part of the target and cannot Correctly estimate the scale of the target

[0005] In view of the fact that the current tracking algorithm cannot deal with the situation that the appearance of the target often changes greatly due to the out-of-plane rotation, and the scale changes greatly, it is necessary to design a tracking algorithm to make it able to deal with the frequent occurrence of the target on the sea surface due to the out-of-plane rotation. When the appearance of the target changes greatly and the scale changes greatly, the robust and adaptive estimation of the scale change of the sea surface target improves the tracking accuracy of the tracker

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