Visual target tracking method based on credibility combination map model

A map model, target tracking technology, applied in image enhancement, image analysis, image data processing and other directions, can solve problems such as limiting the performance of the tracker, the tracker is easy to drift to other objects or backgrounds, and visual tracking drift.

Inactive Publication Date: 2015-03-25
北京交通大学长三角研究院
View PDF2 Cites 11 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

If the target is occluded, part of the information of the target is covered and cannot be represented, that is, the target is represented by some incomplete information, so the tracker will easily drift to other objects or background
In addition, there are many other factors that limi...

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
  • Visual target tracking method based on credibility combination map model
  • Visual target tracking method based on credibility combination map model
  • Visual target tracking method based on credibility combination map model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0046] The visual target tracking method based on the reliability combined map model of the present invention comprises the following steps in turn:

[0047] 1) Establish a training database according to the labeling of the target object in the first frame of the video; the labeling of the target object in the first frame is the input of video tracking.

[0048] 2) Extract the feature (comprising positive feature and negative feature) of training database, train two-dimensional disjunctive unit classifier and two-dimensional disjunctive classifier (online learning algorithm); Extract training database feature and can be known visual feature extraction algorithm , such as Haar features, gradient histogram features, etc.;

[0049] The method for training a two-dimensional disjunctive unit classifier is:

[0050] The set of non-repetitive one-dimensional feature data used in all two-dimensional disjunctive unit classifiers is defined by S={d 1 , d 2 ,...,d w} means that the t...

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 the computer video processing technology, in particular to a visual target tracking method based on a credibility combination map model. The method comprises the following steps that (1) a training database is established; (2) features of the training database are extracted, and a two-dimensional disjunction unit classifier and a two-dimensional disjunction classifier are trained; (3) a credibility combination map of first-frame target objects is established; (4) features of a current-frame background frame are extracted; (5) a credibility graph is obtained; (6) a target is positioned, and a plurality of candidate windows are obtained; (7) a credibility combination map of the candidate windows is matched with the saved previous-frame credibility combination map, and optimal target location information is obtained; (8) an updating sample is obtained by means of combination map matching, and the classifiers, the credibility map model, the state of a tracker and the like are updated every five frames; (9) the step (4), the step (5), the step (6), the step (7) and the step (8) are repeated till a video is over. By means of the visual target tracking method based on the credibility combination map model, the problem of target drifting can be effectively restrained in the computer visual target tracking process, and therefore the stability of the tracker is improved.

Description

technical field [0001] The invention relates to computer video processing technology, in particular to a visual target tracking method based on a reliability combined map model. Background technique [0002] Visual tracking technology is the most industrialized part in the field of computer vision, and is widely used in industry (for example, to calibrate and position materials on an automated production line), military (for example, in the radar field, it is necessary to estimate the position, speed and other motion parameters of the target. ), civil (such as visual tracking in traffic systems can be used to control traffic flow) and other industries. Specific applications include video security surveillance, intelligent robots, behavior analysis, human-computer interaction, etc. [0003] Visual tracking refers to tracking some points or objects of interest in the video, obtaining the position and trajectory of the target, which can establish the basis for subsequent proce...

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
IPC IPC(8): G06T7/20G06K9/66
CPCG06T7/251G06T2207/10016G06T2207/20081
Inventor 滕竹张杰张宁刘峰李浥东王涛
Owner 北京交通大学长三角研究院
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap