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

Target tracking method based on fuzzy learning

A technology of target tracking and fuzzy learning, applied in character and pattern recognition, image data processing, instruments, etc., can solve the problem of low accuracy of illumination change tracking

Active Publication Date: 2017-02-01
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
View PDF5 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in this method, the tracking module is sensitive to illumination changes and the tracking accuracy is not high; the P-N learner rules are simple, and a simple 0-1 hard classification method is used, which is easy to introduce wrong samples into the classifier

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
  • Target tracking method based on fuzzy learning
  • Target tracking method based on fuzzy learning
  • Target tracking method based on fuzzy learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0045] Embodiments of the invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0046] Such as figure 1 As shown, it is a flowchart of the object tracking method based on fuzzy learning in the present invention. It involves three parts: sparse representation tracker, cascade classification detector and fuzzy learner. The three parts are described in detail below.

[0047] 1. Sparse representation tracker

[0048] The tracker includes: using sparse representation technology to extract target features, and using Bayesian classifier to determine the target position.

[0049] In the current frame, the adjacent area corresponding to the target area (based on the tracking window) in the previous frame is used as the candidate area. The adjacent area co...

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 discloses a target tracking method based on fuzzy learning. The method mainly comprises the steps that firstly, a sparse representation algorithm is used to track a target in the aspect of a tracker; secondly, in the aspect of a detector, cascade classification detectors (a variance classifier, a collection classifier and a nearest classifier) are used to detect the target position; and finally, in the aspect of learning update, a fuzzy learner is used to integrate the output results of the tracker and the detector, and the final target position is acquired according to the membership of four constraints of time continuity, the spatial uniqueness , the similarity and the target size consistency. According to the target tracking method based on fuzzy learning, the real-time performance is ensured, and at the same time the adaptability to the target illumination change is great; and the discriminant ability of a learner is improved; the tracking accuracy and robustness of the algorithm are improved; and the method has important theoretical and practical significances for the research and practical application development of target tracking.

Description

technical field [0001] The invention relates to a target tracking method, in particular to a target tracking method based on fuzzy learning, and belongs to the technical fields of computer graphics, digital image processing and pattern recognition. Background technique [0002] In recent years, with the improvement of computer hardware level, the reduction of imaging technology and storage costs, image and video information has been more and more widely used in social life, which has promoted the rapid development of computer vision technology, among which Target tracking based on vision technology has been widely used in intelligent video surveillance, human-computer interaction, robotics and other fields due to its advantages such as wide application environment, high degree of automation, simple information acquisition, and rich information. [0003] Visual object tracking methods can be divided into two categories, namely methods based on visual representation and method...

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): G06T7/292G06K9/46G06K9/62G06K9/66
CPCG06T2207/20081G06V10/40G06V10/513G06V30/194G06F18/24147G06F18/24155
Inventor 周大可徐勇陈志轩杨欣王玉惠
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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