A visual attention detection method based on an improved mixed increment dynamic Bayesian network

A visual attention and dynamic Bayesian technology, applied in instruments, complex mathematical operations, calculations, etc., can solve problems such as low image resolution, low reliability, and inconsistent directions

Active Publication Date: 2019-05-03
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0003] At present, there are two main problems in the detection of visual attention in dynamic scenes and free head postures that need to be studied: under normal eye use, the head posture deflection is consistent with the direction of the line of sight, but the situation that the direction is inconsistent cannot be ruled out, so only the head posture is used. The recognition rate of visual attention is low and the reliability is not high; the impact of low image resoluti

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  • A visual attention detection method based on an improved mixed increment dynamic Bayesian network
  • A visual attention detection method based on an improved mixed increment dynamic Bayesian network
  • A visual attention detection method based on an improved mixed increment dynamic Bayesian network

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[0046] The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

[0047] The technical scheme that the present invention solves the problems of the technologies described above is:

[0048] Technical scheme of the present invention is as follows:

[0049] A visual attention detection method based on an improved hybrid incremental dynamic Bayesian network, comprising the following steps:

[0050] S1, establish three-dimensional face coordinates, and use the geometric relationship model to estimate the line of sight.

[0051] S2, establishing the Bayesian regression posterior probability model of the head estimation sub-model and the line of sight estimation sub-model;

[0052] S3, in order to solve the problem of missing data in continuous time and extreme posture, a p...

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Abstract

The invention requests to protect a visual attention detection method based on an improved mixed increment dynamic Bayesian network. According to the method, a head, a sight line and a prediction sub-model are fused to carry out comprehensive estimation on the visual attention detection method; the sight line detection sub-model is improved on the basis of a traditional human eye model, so that the recognition rate is increased, and the detection robustness of different testers is improved; in order to solve the problem of data loss caused by extreme postures and dynamic scenes, a prediction sub-model is provided, and the correlation of sampling pictures at two moments is measured by utilizing a Gaussian covariance, so that the false recognition at the current moment is effectively improved, and the recognition error is reduced. Secondly, describing the related sub-models, and respectively establishing Bayesian regression models by utilizing conditional probabilities; And parameters ofthe model are dynamically updated by using an incremental learning method, so that the adaptability of the whole model to new input data is improved.

Description

technical field [0001] The invention belongs to the field of image processing and pattern recognition, in particular to a visual attention detection method based on an improved hybrid incremental dynamic Bayesian network. Background technique [0002] Visual focus of attention (VFOA) specifically refers to the direction and target that the human eye pays attention to. It represents the direction a person is looking at, and contains rich information, such as: what the person is interested in, what he is doing, etc. This information has high application value for fields such as human-computer interaction, intelligent assisted driving, medical research, psychology, and market analysis. In recent years, especially in terms of human-computer interaction, visual attention, as an input method to assist other commands, and judge whether it is a user or other human obstacles in front of it, so as to realize intelligent bionic obstacle avoidance, has attracted extensive attention fro...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06F17/16
CPCY02T10/40
Inventor 罗元陈雪峰张毅陈旭刘星遥
Owner CHONGQING UNIV OF POSTS & TELECOMM
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