Rigid body target tracking method based on feature and region collaborative matching

Abstract

The invention relates to a rigid body target tracking method based on feature and region collaborative matching. The method comprises the steps of 1) selecting a target area in an initial image, and detecting SURF features in the target area; 2) constructing an invariance area with each SURF feature point as a center in the target area; 3 ) extracting the SURF features of a current image when thecurrent image arrives, and forming matching point pairs by carrying out cooperative matching on the current image and the initial image based on the SURF feature and the invariance region; 4 ) calculating to obtain motion parameters according to the obtained matching point pairs, so as to determine the target area of the current image and realize target tracking. The repeatability rule of SURF features under the complex change is studied, the motion parameters are solved through a scheme that the SURF features and the regional templates are matched in a collaborative mode, accurate descriptionand matching can be achieved on the local features of the target region, and the robustness and the stability of the target tracking effect are guaranteed.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a rigid object tracking method based on feature and region collaborative matching. Background technique [0002] The movement of any point on the surface of a rigid target can represent the movement of the whole, which makes it possible to use the features in the target area to describe the movement of the target. Existing rigid-body target tracking methods are dedicated to extracting some invariant features in the target region of the reference image, and quantifying and describing the extracted features, such as color features, texture features, and optical flow features. Local features refer to the invariant, reproducible, and specific features detected in the image area, which can resist complex changes such as occlusion, scale, and rotation to a certain extent, and provide quantitative descriptions of features. At present, compared with other features, l...

AI Technical Summary

Problems solved by technology

[0009] In the video, one or more changes such as illumination, occlusion, viewing angle, affine, etc. often occur in the scene, which seriously interferes with the matching of local features.

The existing technology still uses the same local feature matching method as the static image, which cannot adapt to the scene with drastic changes, nor does it reflect the adaptability corresponding to the continuous change of the scene

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