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An adaptive fusion complementary learning real-time tracking method based on a target probability model

A probabilistic model and real-time tracking technology, applied in image analysis, instruments, calculations, etc., can solve problems such as damage tracker performance, target loss, etc., and achieve the effect of versatility and excellent performance

Active Publication Date: 2019-06-28
FUJIAN INST OF RES ON THE STRUCTURE OF MATTER CHINESE ACAD OF SCI
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  • Abstract
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AI Technical Summary

Problems solved by technology

[0004] In terms of multi-feature response fusion, most current tracking methods use fixed fusion coefficients, which will have different effects on feature fusion in different situations, and even seriously damage the performance of the tracker, resulting in target loss.

Method used

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  • An adaptive fusion complementary learning real-time tracking method based on a target probability model
  • An adaptive fusion complementary learning real-time tracking method based on a target probability model
  • An adaptive fusion complementary learning real-time tracking method based on a target probability model

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Embodiment Construction

[0058] The present application is described in detail below in conjunction with the examples, but the present application is not limited to these examples.

[0059] see figure 1 , the adaptive fusion complementary learning real-time tracking method based on the target probability model provided by the application includes the following steps:

[0060] Step S100: Take the target position P in the t-1 frame image t-1 As the center, take the t-1 frame image size as the matching area size, and generate a search area within a multiple of the target size in the t-1 frame image;

[0061] Step S200: Obtain the directional gradient histogram feature of the search area o and compare it with the t-1 frame image I t-1 The generated directional gradient histogram features are matched to obtain the second matching value matrix, and the second matching value matrix is ​​used as the directional gradient histogram matching value matrix r cf ;

[0062] Step S300: Obtain the color histogram ...

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Abstract

The invention discloses an adaptive fusion complementary learning real-time tracking method based on a target probability model. According to the method, on the basis of complementarity real-time tracking (Spacle) of a current mainstream tracking method, the real-time tracking is realized; a piecewise function is innovatively used, an average value adaptive fusion coefficient is used when a tracking target foreground ratio obtained by using color histogram features is smaller than a piecewise function threshold value, and an index adaptive fusion coefficient is used when the tracking target foreground ratio obtained by using the color histogram features is larger than or equal to the piecewise function threshold value. Therefore, an appropriate piecewise function threshold value is selected according to different video attributes. The method is excellent in performance, has universality and can be used for solving the similar multi-feature fusion problem.

Description

technical field [0001] The present application relates to an adaptive fusion complementary learning real-time tracking method based on a target probability model, which belongs to the field of machine vision target tracking. Background technique [0002] Visual tracking technology is a hot and difficult point in the field of computer vision research. At the same time, it has broad prospects for commercial application and is widely used in human-computer interaction, public safety, medical imaging and other fields. [0003] At present, the commonly used target tracking methods use the orientation gradient histogram and color features of the target image to achieve complementary advantages. Using the color feature to compensate for the orientation gradient histogram can only extract the target space information and must ignore the color feature. Although the orientation gradient histogram is used to compensate for the color features, but can only extract the target color infor...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/246G06T7/90
Inventor 董秋杰周盛宗何雪东葛海燕
Owner FUJIAN INST OF RES ON THE STRUCTURE OF MATTER CHINESE ACAD OF SCI
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