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Clustering subdomain association-based stable characteristic mining and target tracking method

A target tracking and sub-region technology, which is applied in the field of video image processing, can solve the problems of inconsistency in the changing rules of the position of the color center of mass, update errors, etc., to improve clustering efficiency and adaptability, reduce time consumption, and improve tracking robustness Effects on Sex and Accuracy

Active Publication Date: 2016-04-20
南京雷斯克电子信息科技有限公司
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Problems solved by technology

However, changes in the target's appearance scale and attitude may cause inconsistencies in the change of the position of some color centroids, causing update errors

Method used

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  • Clustering subdomain association-based stable characteristic mining and target tracking method
  • Clustering subdomain association-based stable characteristic mining and target tracking method
  • Clustering subdomain association-based stable characteristic mining and target tracking method

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Embodiment Construction

[0027] The present invention will be further described below in conjunction with the accompanying drawings.

[0028] Such as figure 1 As shown, the stable feature mining and target tracking method based on clustering sub-region association, the specific implementation steps are as follows:

[0029] Step 1: Use the threshold and background update adaptive background difference algorithm to detect the target moving area; according to the peak profile, candidate peak, regional peak and residual peak energy of the V color component histogram of the target moving area, adaptively obtain the number of clusters. The specific steps to calculate the number of clusters are as follows:

[0030]1a. Extracting the V color component histogram peak profile of the target motion detection area;

[0031] 1b. Use a median filter with a window length of n=2, n∈[2,5] to smooth the peak profile to highlight the main peak Where l1=1,2..., L1 is the number of the main peak;

[0032] 1c. will be ...

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Abstract

The invention discloses a clustering subdomain association-based stable characteristic mining and target tracking method. The method comprises the steps of a) detecting a target motion region in the self-adaptive manner, extracting the histogram peak profile of a V-color component in the target motion region, and acquiring the number of clusters according to the energy of a candidate peak, the energy of a region peak and the energy of residual peaks; b) constructing a sample grayscale matrix for an S component and a V component in the target motion region, and conducting the class-number-adaptive K-means clustering operation; c) marking class-based connected sub-regions and establishing the sub-region template, observation model and incremental model description; d) establishing the relationship between a target template and a current observed model sub-region, mining the characteristics of a template-observation stable sub-region and the change rate of template characteristics; e) fusing the displacements in all stable sub-regions of the template, the center of a target detection region and the track of a previous frame in the weighted manner so as to locate the current track of a target, and updating the target template according to the weighted average increment of stable characteristics and the change rate thereof in the frame-by-frame manner.

Description

technical field [0001] The invention relates to a stable feature mining and target tracking method based on clustering sub-region association, and belongs to the technical field of video image processing. Background technique [0002] Object visual feature modeling is one of the key technologies in the field of object recognition and tracking. At present, scholars at home and abroad are mainly concerned with the improvement of the robustness and accuracy of the target feature model under the conditions of target pose and scale changes, scene illumination changes and scene interference. [0003] Since the feature description method based on the target content information was reported, there have been continuous patent reports on improving this route at home and abroad, which can be summarized into three categories: 1. Improvement of the description method based on the target’s own spatial feature information; 2. Fusion of the target’s own spatial feature and The description ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46
CPCG06V20/46G06V20/41G06V10/56
Inventor 路红李宏胜刘大伟汤皓宗成成
Owner 南京雷斯克电子信息科技有限公司
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