Camshift algorithm for tracking centroid correction model on the basis of Grabcut and LBP (Local Binary Pattern)

Abstract

The invention discloses a Camshift algorithm for tracking a centroid correction model on the basis of Grabcut and an LBP (Local Binary Pattern). A target object is separated from an environment through the constant value enhancement tracking of a video stream and Grabcut foreground segmentation to cause the Camshift to obtain a pure histogram. Meanwhile, a Kalman filter assists the Camshift to predict a target movement locus. During a tracking period, carrying out LBP transform on an image in a target frame to obtain a template and current LBP histogram data, a judgment coefficient and a frame body change situation are obtained through comparison, and an S-Grabcut algorithm is executed if the target object is blocked by an object with a similar color, a centroid is removed, and normal tracking is continuously carried out. Compared with a traditional Camshift algorithm, the algorithm disclosed by the invention reduces the interference of background noise to a large extent, and the problem of quick movement and blocking is solved since the Kalman filter is added. Meanwhile, interference brought by the blocking of the object with the similar color can be favorably solved by the centroid correction model. Experiment results indicate that the algorithm has good robustness, meets the requirements of instantaneity and accuracy in tracking and causes the target to be more stably tracked under a complex environment.

Description

technical field [0001] The invention belongs to the field of computer vision real-time tracking. Background technique [0002] Visual tracking plays a very important role in image processing and computer vision, and is one of the hot research directions at present. Moving target tracking is widely used in many fields such as military and civilian, such as visual guidance, UAV tracking, security monitoring, public scene monitoring, intelligent transportation, etc. However, the pictures captured by the camera are very susceptible to interference from many factors such as lighting changes, object movement speed, occlusion, and similar colors. At present, the commonly used methods for tracking moving targets include particle filter, compressed sensing, background difference method, adjacent frame difference method, optical flow method and adaptive mean shift algorithm (Camshift) and so on. However, these methods have their own advantages and disadvantages. For example, the opt...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to propose a Camshift algorithm based on Grabcut (image segmentation) and LBP (local binary pattern) tracking centroid correction model to solve the problem of bac

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