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Target tracking method based on Bayesian learning and incremental subspace learning

A Bayesian learning and target tracking technology, which is applied in the field of target tracking based on Bayesian learning and incremental subspace learning, can solve problems such as tracking drift and reduce the learning rate of discriminant algorithms

Active Publication Date: 2017-01-25
南京孚光信息科技有限公司
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Problems solved by technology

In order to solve this problem, when the credibility of the tracking results decreases, the learning rate of the discriminant algorithm is reduced and the model algorithm is suspended to update the generation model to avoid introducing more noise. It overcomes the problem of tracking drift after the target suffers from occlusion and huge deformation. question

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  • Target tracking method based on Bayesian learning and incremental subspace learning
  • Target tracking method based on Bayesian learning and incremental subspace learning
  • Target tracking method based on Bayesian learning and incremental subspace learning

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Embodiment Construction

[0046] Such as figure 1 , a target tracking method based on Bayesian learning and incremental subspace learning, including the following steps:

[0047] Step 1, initialization; said initialization includes the following steps;

[0048] Step 1.1, initialize the target position in the first frame; set t=1 to enter the first frame, mark the initial position R of the target with a rectangular frame g =g x , g y , g w , g h , where g x , g y Represents the coordinates of the center position of the target rectangle, g w , g h are the width and height of the target rectangle;

[0049] Step 1.2, initialize particles and motion parameters; make Indicates the vector variable describing the particle state, the superscript (i) indicates the i-th particle, Represents the position coordinates of the x and y directions represented by the particle, Indicates the scale of the box represented by the particle in the x and y directions;

[0050] The state of the initial particle i...

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Abstract

The invention provides a target tracking method based on Bayesian learning and incremental subspace learning. Tracking results are produced through mixing of two sub-algorithms respectively, credibility measurement of the two candidate tracking results is defined with the method, and the algorithm with the highest credibility is selected as the tracking result finally; on the basis of reduction of the learning rate of a discrimination algorithm and suspending of updating of a model generating algorithm, introduction of more noise is avoided, and the problem of tracking drift caused after a target is shaded and subjected to large deformation is solved.

Description

technical field [0001] The invention belongs to the field of computer vision and pattern recognition, and in particular relates to a target tracking method based on Bayesian learning and incremental subspace learning. Background technique [0002] Object tracking has a wide range of applications in real-time monitoring, motion capture, video analysis and entertainment, and is one of the most active fields in computer vision. In recent years, a large number of tracking algorithms have been developed at home and abroad, but due to the lack of information of the target itself, as well as the influence of various factors such as illumination changes, occlusion, and target rotation, it is still an important task to develop a tracking algorithm with robust effects. Challenging research topics. [0003] Model-free object tracking only gives the location of the object in the first frame, and the tracking algorithm is responsible for finding the location of the object in the rest of...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/217
Inventor 何发智李康
Owner 南京孚光信息科技有限公司
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