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Camshift algorithm based on grabcut and lbp tracking centroid correction model

A centroid and model technology, applied in the field of computer vision real-time tracking, can solve problems such as interference of objects of similar color, and achieve the effect of increasing contrast and eliminating background noise.

Active Publication Date: 2018-08-17
NANCHANG UNIV
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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 background noise and similar color object interference in the tracking process. This algorithm can perform real-time Track and greatly improve the stability and accuracy of traditional Camshift algorithm tracking

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  • Camshift algorithm based on grabcut and lbp tracking centroid correction model
  • Camshift algorithm based on grabcut and lbp tracking centroid correction model

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[0018] The above and other features and advantages of the present invention can be more clearly understood through the following detailed description in conjunction with the accompanying drawings.

[0019] The algorithm of the invention is mainly researched and improved on the basis that the fusion algorithm of Camshift and Kalman cannot stably achieve accurate real-time tracking effect on the background noise influence and similar color interference problems.

[0020] The Grabcut segmentation algorithm is used to segment the target in the enhanced video stream to obtain a pure target histogram, which reduces the tracking deviation. Based on the Camshift algorithm, the image is converted from the RGB color space to the HSV color space (H means chroma, S means saturation, V means brightness), and we use the histogram of the H component (strong robustness to light) to establish In the color probability target model, the pixel value of the original image is replaced by the statis...

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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...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/207G06T5/20
CPCG06T5/40
Inventor 洪向共郑熙映薛志毅肖惠梅
Owner NANCHANG UNIV
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