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A Compressed Sensing Tracking Method Based on Hybrid Classifier Decision

A hybrid classifier and compressed sensing technology, applied in the field of computer vision, can solve problems such as missing targets, achieve the effect of reducing update errors and improving robustness

Inactive Publication Date: 2018-03-02
WUHAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

If the target experiences long-term occlusion, the classifier will learn too much occlusion information, and finally lead to missing the target

Method used

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  • A Compressed Sensing Tracking Method Based on Hybrid Classifier Decision
  • A Compressed Sensing Tracking Method Based on Hybrid Classifier Decision
  • A Compressed Sensing Tracking Method Based on Hybrid Classifier Decision

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Embodiment Construction

[0057] In order to make the purpose, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0058] The compressive sensing method is a simple and efficient tracking method based on compressive sensing theory. The main idea is to extract the features of the foreground target and background information through the sparse measurement matrix that satisfies the RIP condition as the positive and negative samples of the online learning update classifier, and then use the trained Naive Bayes in the image of the next frame The classifier locates the target.

[0059] please see figure 1 , a compressive sensing tracking method based on hybrid classifier decision-making provided by the present ...

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Abstract

The invention discloses a hybrid classifier decision-based compressed sensing tracking method, which mainly solves problems that after drift happens to a region selected to be positioned by the tracking method in the prior art, the classifier becomes inaccurate, and after a target is seriously blocked for a long period of time, the target is likely to get lost. Two classifiers are defined by the method of the invention; the two classifiers are respectively an original classifier before the target is blocked and a new classifier when the target enters a blocked state; when the target is detected to be blocked, the classifier state for previous iterative training is kept; a new classifier is initialized at the current frame; the two classifiers are selectively used for target tracking; and finally, a different mode is used for updating the two classifiers to achieve the purpose of correctly tracking the target by the method. Updating errors of a target appearance model can be effectively reduced, and thus robustness of the compressed sensing method in complicated scenes is improved.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a compression sensing tracking method based on mixed classifier decision-making in the technical field of digital image target tracking. Background technique [0002] In recent years, object tracking has become a hot research issue in the field of computer vision [Document 1]-[Document 2], and has been widely used in practical fields such as automatic monitoring, video retrieval, and traffic supervision. Object tracking is the task of estimating the state of an object in subsequent video sequences given the initial state of the object in the first frame. In the past few decades, researchers have proposed many methods [Document 3]-[Document 15], but because the tracking method is affected by many factors, especially the appearance changes caused by posture, illumination and occlusion, so far no There exists a single tracking method that can successfully handle...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/246G06K9/62G06T7/20
CPCG06T7/20G06T2207/10016G06T2207/20081
Inventor 李晶孙航常军杜博苏振扬肖雅夫
Owner WUHAN UNIV