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

Static target segmentation method based on Gauss background model

A Gaussian background model, target segmentation technology, applied in image analysis, character and pattern recognition, image data processing and other directions, to achieve the effect of maintaining photometric sensitivity and learning ability

Inactive Publication Date: 2012-06-20
SHANGHAI JIAO TONG UNIV
View PDF2 Cites 22 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to address the deficiencies in the prior art and propose a method for static object segmentation based on the Gaussian background model, which is an improved algorithm for the current Gaussian background model. Issues with light and shadow effects

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
  • Static target segmentation method based on Gauss background model
  • Static target segmentation method based on Gauss background model
  • Static target segmentation method based on Gauss background model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0016] In order to better understand the technical solution of the present invention, the calculation process of the following formulas will be further described in detail in conjunction with the accompanying drawings. Specifically, proceed as follows:

[0017] 1. Construct a Gaussian model based on the first frame of the video or a preloaded background image, construct K Gaussian model distributions for each color channel of each pixel (a total of RGB3 channels) (K usually takes 3~5), and load the corresponding initialization parameters , including learning rate win_size (that is, the number of frames required for initial background model learning), matching threshold std_threshold , weight threshold bg_stdshold, variance initial value var_init, target minimum area threshold area_threshold , the number of repetitions counter counter threshold counter_threshold, initialize the Gaussian distribution model of each point. The corresponding mean of the first distribution is u...

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 relates to a static target segmentation method based on a Gauss background model. The method comprises the following steps of: firstly, building a Gauss mixed model according to a first frame or pre-loading background, and loading parameter thresholds of the required parts; when a new frame enters, scanning pixel by pixel and matching with a background model; if distribution which is the same with a previous frame is matched, adding one, otherwise returning to zero; then carrying out parameter updating according to a matching result, sorting updated distributions, and selecting a corresponding generated background model according to the thresholds; filtering a smaller foreground part by finding a connected domain, so as to eliminate interference of noise and complicated background, and carrying out parameter restoration pixel by pixel according to an accumulated value of a counter at the foreground part. By applying the method provided by the invention, the Gauss background model can effectively identify a static object which enters into a background for a long time, and sensitivity identification to luminosity and learning capability of the Gauss background model can be maintained.

Description

technical field [0001] The invention relates to the field of advanced manufacturing and automation, more specifically, to a Gaussian background model method capable of segmenting and extracting static foreground objects. Background technique [0002] In video surveillance, in order to extract the target object, the method of building a background model is often used. The most typical one is the Gaussian background model algorithm (Chris Stauffer and W. Eric L. Grimson, " Learning Patterns of Activity Using Real-Time Tracking ," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 747–757, 2000. Pattern behavior learning using real-time tracking methods), which can construct several Gaussian distributions for each pixel, Perform matching to determine whether it belongs to the foreground, and continuously update the parameters of the Gaussian distribution, thereby effectively solving the problems of background noise and illumination. [0003] S...

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 Applications(China)
IPC IPC(8): G06K9/00G06T7/00
Inventor 厉鹏王宸昊王冲鶄刘允才
Owner SHANGHAI JIAO TONG UNIV
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