Weighting local multi-task sparse tracking method with robustness

A multi-tasking, robust technique for computer vision

Inactive Publication Date: 2017-10-20
ZHEJIANG NORMAL UNIVERSITY
View PDF0 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In view of the important application value of visual tracking technology, both academia and industry have conducted extensive research on it, but due to the existence of complex scenes such as ill

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
  • Weighting local multi-task sparse tracking method with robustness
  • Weighting local multi-task sparse tracking method with robustness
  • Weighting local multi-task sparse tracking method with robustness

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0061] Embodiment 1. Robust weighted local multi-task sparse tracking method

[0062] figure 1 It is a schematic flow chart of a tracking method according to an embodiment of the present invention, which mainly includes the following steps:

[0063] Step S200, initialize the target template, that is, in the first frame, initialize the target template T=[T 1 ,T 2 ,...,T m ];

[0064] Step S210, divide each of the above-mentioned target templates into K sub-blocks, and use the k-th local blocks of the m target templates to obtain the corresponding template dictionary Among them, k=1,...,K, Represents the color histogram feature corresponding to the k-th local block in the i-th target template;

[0065] Step S220, using Gaussian random sampling (taking the state variable of the t-1th frame as the mean value and the constant as the variance) to obtain n candidate target particles in the tth frame;

[0066] Step S230, using the same blocking method as the above-mentioned 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 discloses a weighting local multi-task sparse tracking method with robustness, so as to improve the accuracy rate of tracking a target object. The method is as follows: target templates are initialized, and block processing is performed on each target template, thereby obtaining a template dictionary; candidate target particles of a current frame are obtained based on a Gaussian random sampling principle, and block processing is also performed on the candidate target particles; the candidate target particles after block processing is linearly expressed based on the template dictionary; stray particles are captured based on a local multi-task sparse tracking model; a globally optimal solution of the tracking model is obtained based on a near-end gradient acceleration method; a tracking result is determine according to observation likelihood; and the template dictionary is updated on line. The weighting local multi-task sparse tracking method with robustness can effectively prevent a phenomenon that tracking is inaccurate or even drifts from being caused by illumination variation, scale variation, sheltering, deformation, rapid movement, rotation and background clutter of a target object in a video, thereby achieving the purpose of accurately and continuously tracking the target object in the video.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a robust weighted local multi-task sparse tracking method. Background technique [0002] Visual tracking is an important link in the computer vision system, which integrates the theoretical knowledge of image processing, stochastic process, artificial intelligence and other fields. Visual tracking is to use the color, texture, edge and outline of the specified target object to detect the target object, use the tracking algorithm to estimate the current motion state of the target object, and predict the state at the next moment, so as to realize the specified Accurate, continuous and fast tracking of target objects. The key to visual tracking is to establish the corresponding relationship between the candidate target in each frame image and the pre-determined tracking target. [0003] Visual tracking technology has broad application prospects and huge market demand. At present, it...

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): G06T7/269G06T7/277G06K9/62
CPCG06T7/269G06T7/277G06T2207/10016G06F18/21345
Inventor 熊继平叶童王妃
Owner ZHEJIANG NORMAL UNIVERSITY
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