Target tracking method based on character feature invariant and graph theory clustering

Abstract

The invention relates to a target tracking method based on character feature invariant and graph theory clustering which overcomes the defects of the existing target tracking method, tackles the difficult problems of target scale changes, rotations, noises, diurnal variations, shadings, conglutinations, camera vibrations and the like in a scene and generates the stable target trajectory and accurate motion information thereof. The tracking method comprises the following steps: demarcating a camera; preprocessing an image; detecting a character; extracting invariant features, calculating invariant features at angular points; matching features, performing invariant feature matching between one angular point of the previous frame and all the angular points of the neighborhood of the local frame; forming angular point trajectories, connecting the frame-matched angular points to form the trajectory of the angular point; performing trajectory clustering based on graph theory, forming a plurality of temporary targets after clustering; combining or splitting targets, determining that the target and the temporary targets obtained by clustering combine or split, performing reasonableness test to update the current set target; performing reasonableness test to judge the reasonableness of the trajectory and scale of the target; and extracting Gaussian background and angular point background.

Description

technical field The invention relates to a target tracking method, in particular to a target tracking method based on corner feature invariants and graph theory clustering. Background technique Object tracking has a very wide range of research and applications in the fields of visual navigation, behavior recognition, intelligent transportation and so on. Most of the current motion detection and target tracking algorithms are based on the mixed Gaussian background model, but it is difficult to solve problems such as illumination changes, occlusion, adhesion, and camera shake; other tracking algorithms such as optical flow tracking, tracking based on edge models, based on Feature tracking, CamShift tracking, etc., cannot solve the above problems well; in fact, there are more problems in the field of target tracking, such as scale change, rotation, noise, shadows, etc., and there have been no good solutions. With the research of image invariant characteristics and the applicat...

AI Technical Summary

Problems solved by technology

Technical problem: the technical problem to be solved by the present invention is: overcome the deficiency of existing target tracking method, provide a kind of target tracking method based on corner feature invariant and graph theory clusterin...

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