Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Object Tracking Method Based on Bayesian Learning and Incremental Subspace Learning

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

Active Publication Date: 2019-08-20
南京孚光信息科技有限公司
View PDF8 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Object Tracking Method Based on Bayesian Learning and Incremental Subspace Learning
  • Object Tracking Method Based on Bayesian Learning and Incremental Subspace Learning
  • Object Tracking Method Based on Bayesian Learning and Incremental Subspace Learning

Examples

Experimental program
Comparison scheme
Effect test

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...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides a target tracking method based on Bayesian learning and incremental subspace learning. The two sub-algorithms are mixed to generate a tracking result respectively. The invention defines the credibility measure of the two candidate tracking results, and the final algorithm selection The algorithm with the highest reliability is used as the tracking result; the invention avoids introducing more noise by reducing the learning rate of the discriminant algorithm and pausing to update the generation model algorithm. It overcomes the problem of tracking drift after the target suffers from occlusion and huge deformation.

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

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(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
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com