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

Particle swarm optimization algorithm-based tracking and locating method for multiple moving targets in video

A particle swarm optimization, multi-moving target technology, applied in the field of image processing, can solve problems such as inability to solve multi-target positioning problems, and achieve the effects of solving stagnation, reducing computing costs, and low spatial resolution

Inactive Publication Date: 2017-09-15
NANJING UNIV OF POSTS & TELECOMM
View PDF4 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, the traditional particle swarm optimization algorithm can only solve the single target localization problem, but cannot solve the multi-target localization problem.

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
  • Particle swarm optimization algorithm-based tracking and locating method for multiple moving targets in video
  • Particle swarm optimization algorithm-based tracking and locating method for multiple moving targets in video
  • Particle swarm optimization algorithm-based tracking and locating method for multiple moving targets in video

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0033] Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:

[0034] Such as figure 1 Shown is the flow chart of the video multi-moving target positioning method based on the particle swarm optimization algorithm, as follows:

[0035] Step 1), select a section of 8046-frame video sequence from the vehicle monitoring video, compress the size to 240x320 pixels, and evenly extract 40 frames of the video at the same time interval. Access each pixel of each frame by row, record the color intensity values ​​of the three channels of each pixel, calculate the grayscale value of each pixel, and convert each frame of image into a two-dimensional matrix, The two-dimensional matrix converted from the i-th frame image is denoted as I i , i∈{1,2,…,40};

[0036] Step 2), sequentially divide each frame of image into speckle pixels and background pixels to obtain the number of speckles and their geometric features, the spe...

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 particle swarm optimization algorithm-based tracking and locating method for multiple moving targets in a video. The method comprises the steps of firstly performing image spot detection through a background difference method; and then dividing a particle swarm into particle sub-swarms by taking image spots as units, and using a particle swarm optimization algorithm in each sub-swarm to achieve the purpose of locating the multiple moving targets. According to the method, the problem of multi-target detection and the problem of difficult detection caused by object adhesion and picture darkness can be solved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a video multi-moving target tracking and positioning method based on particle swarm optimization algorithm. Background technique [0002] Particle Swarm Optimization (PSO) is inspired by the regularity of bird cluster activities, and then uses swarm intelligence to establish a simplified model. Based on the observation of the behavior of animal clusters, particle swarm algorithm uses the information sharing of individuals in the group to make the movement of the whole group evolve from disorder to order in the problem solving space, so as to obtain the optimal solution. Similar to genetic algorithm, PSO is an optimization algorithm based on iteration. The system is initialized as a set of random solutions, and the optimal value is searched through iteration. But it does not have the crossover and mutation used by the genetic algorithm, but the particles follow the opti...

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): G06T7/246G06N3/00
CPCG06T7/251G06N3/006G06T2207/10016
Inventor 张姝彦岳文静陈志董聪薛丽
Owner NANJING UNIV OF POSTS & TELECOMM
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