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A Visual Mapping Method Based on Support Vector Regression

A technology of support vector regression and mapping methods, which is applied in instrumentation, computing, character and pattern recognition, etc., and can solve problems such as poor visual mapping estimation effect.

Active Publication Date: 2019-11-05
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

This invention patent solves the problem of poor estimation effect of existing visual mapping methods in the case of a small number of training samples

Method used

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  • A Visual Mapping Method Based on Support Vector Regression
  • A Visual Mapping Method Based on Support Vector Regression
  • A Visual Mapping Method Based on Support Vector Regression

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Embodiment Construction

[0046] Implementation language: Matlab, C / C++

[0047] Hardware platform: Intel core2 E7400+4G DDR RAM

[0048] Software platform: Matlab2012a, VisualStdio2010

[0049] According to the method of the present invention, first clearly the visual mapping problem to be solved, and collect relevant images (head image, body image and facial image, etc.) and calibrate the target value (head posture angle, body posture angle and age). According to the patent of the present invention, first use Matlab or C language to write a program to learn the support vector regression model from the image to the target value; then visually map the input image to be estimated to estimate the target value. The method of the invention can be used for visual mapping problems in various computer visions, and can solve the problem of limited training samples in practical applications.

[0050] A visual mapping method based on support vector regression:

[0051] Step 1: Collect N input images according...

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Abstract

The invention discloses a visual mapping method based on support vector regression. Visual mapping refers to a method for estimating corresponding continuous target values ​​from input image features, such as posture estimation, age estimation, and line of sight tracking. A common problem in practical applications is that the number of training samples is limited when training the mapping model. This patent proposes to use the support vector regression model to learn the visual mapping relationship, which can effectively overcome the above problems. When a new sample is given, it directly uses the learned linear regression function with a kernel function to predict the target value. The training time of this method is short, and the operation is convenient and simple.

Description

technical field [0001] The invention belongs to the technical field of computer vision, relates to visual mapping technology, and is mainly used in visual problems such as posture estimation, line of sight tracking, age estimation and body posture estimation. Background technique [0002] In computer vision, visual mapping refers to the process of learning a mapping function between input image features and output variables, so that when a new image is input, the target output value corresponding to the input image is estimated. Specifically, visual mapping includes: human body pose estimation, head pose estimation, line of sight estimation, and object tracking. See references for details: O.Williams, A.Blake, and R.Cipolla, Sparse and Semi-Supervised Visual Mapping with the S3GP, in IEEEConference Computer on Computer Vision and Pattern Recognition, pp.230-237, 2006. [0003] As an important branch of computer vision, visual mapping has changed the situation where humans e...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06V10/758G06F18/213G06F18/2411
Inventor 潘力立
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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