Kernel correlation filtering target tracking method based on feature fusion and Bayesian classification

A technology of Bayesian classification and kernel correlation filtering, applied in the field of target tracking, which can solve the problems of reduced overall performance of the algorithm, inability to handle target scale changes, insufficient description of target appearance, etc.

Active Publication Date: 2017-04-19
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

[0008] However, there are two deficiencies in the method based on correlation filtering. First, the features used by this type of algorithm are relatively simple, and the description of the target appearance is often insufficient; when the target appearance is disturbed, a single feature is easy to change, resulting in The overall performance of the algorithm is reduce

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  • Kernel correlation filtering target tracking method based on feature fusion and Bayesian classification
  • Kernel correlation filtering target tracking method based on feature fusion and Bayesian classification
  • Kernel correlation filtering target tracking method based on feature fusion and Bayesian classification

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[0082] This implementation is based on feature fusion and Bayesian classification of kernel correlation filtering target tracking method, the process is as follows figure 1 shown; includes the following steps:

[0083] Step 1, set t=1, capture the first frame of video image, select the rectangular area of ​​the target to be tracked, and obtain the center position and scale of the target;

[0084] Step 2: Take the target center position of the t-th frame video image as the center, extract a sub-window twice the target scale; establish a target appearance model x for the sub-window image; train the correlation filter A and the color Bayesian model R b ;

[0085] The third step is to judge the value of t: if t=1, the target appearance model x is used as the standard appearance model Train the correlation filter A as the standard correlation filter Color Bayesian Model R b As a standard color Bayesian model If t>1, update the standard appearance model and standard correl...

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Abstract

The invention provides a kernel correlation filtering target tracking method based on feature fusion and Bayesian classification. The method is characterized by comprising steps: firstly, the position and the scale information of an initial frame target are given; then, a standard target appearance model, a standard correlation filter and a standard color Bayesian model are built or updated; based on the target center point of a former frame, a search area is extracted; and a correlation filter of a Gaussian kernel is used for realizing target displacement estimation, the color Bayesian model is used for realizing target scale estimation, and the tracking result of the current frame is obtained further. Through sequentially processing each frame of video image, tracking of a moving target in the video is realized. The target tracking method can effectively solve the problem of precisely positioning the moving target in the video and can realize target scale estimation. In multiple challenging environments, the target can be tracked effectively, certain robustness is realized, and the tracking algorithm precision is improved.

Description

technical field [0001] The present invention relates to the technical field of target tracking, and more specifically relates to a kernel correlation filter target tracking method based on feature fusion and Bayesian classification. Background technique [0002] Target tracking algorithm is a popular research direction in the field of computer vision at present. Its main task is to continuously track the target of interest online in a given video sequence, so as to obtain information such as the position of the target in each frame of image. In recent years, object tracking technology has been widely used in many fields such as video surveillance, human-computer interaction, and video content analysis. Although so far, researchers have proposed many solutions. However, due to the need to consider various interference factors during the movement of the target, such as: self-deformation, illumination changes, background noise, occlusion, etc., it is still a considerable chall...

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Application Information

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IPC IPC(8): G06K9/00G06K9/20G06K9/62G06K9/48
CPCG06V20/40G06V10/22G06V10/473G06V10/46G06F18/214
Inventor 康文雄施睿吴桂乐
Owner SOUTH CHINA UNIV OF TECH
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