Method for tracking target object

A target and object technology, applied in the field of tracking, can solve problems such as simple algorithms, features being affected by the background, and complex TLD calculations, and achieve the effect of strong practicability and full-automatic tracking

Active Publication Date: 2018-06-29
XIAMEN MEIYA PICO INFORMATION
View PDF15 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] Traditional vision-based target tracking algorithms include motion detection, MeanShift, KCF, TLD, etc. The motion detection algorithm is simple and cannot detect scenes with background changes; MeanShift and KCF have a small amount of calculation, and the tracking effect is good for situations where the target size does not change much. , TLD is complex to calculate. Like KCF, it has requirements for the size change of the target. For fast and small target objects, due to the small size, the features are easily affected by the background, and the tracking effect is poor. KCF and TLD are a kind of The online learning algorithm needs to manually specify the object to be tracked before starting to track. It cannot achieve automatic tracking, and the target needs to be specified again after the target is lost. The practical effect is poor.

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
  • Method for tracking target object
  • Method for tracking target object
  • Method for tracking target object

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] Please refer to figure 1 , Embodiment 1 of the present invention is:

[0047] A method for tracking a target, comprising the following steps:

[0048] S1. Pre-acquisition of multiple frames of images at one position, performing average calculation on the gray value of the same pixel in each frame of images, and calculating the first image, which is marked as a background image. This embodiment collects 20 frame image;

[0049] Collecting a frame of images at the one position, detecting moving objects in the one frame of images, and obtaining a set of moving objects;

[0050] Comparing the one-frame image with the background image, judging whether there is an object in the one-frame image that does not exist in the background image, and if so, the object is a moving object;

[0051] judging whether there is the same object whose vector displacement difference between the one frame image and the background image is greater than the first preset value, if so, judging th...

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 method for tracking a target object. The method comprises the steps of collecting a moving object set of a frame image at a position; identifying a moving object matched with a target object with rapid speed and small size; adjusting the position of the target object on the frame image and an adjustment angle of a collection range so that the target object is kept in a central region of the collection range and the tracking for the target object with rapid speed and small size is realized. With the method, automatic tracking can be implemented and the practicabilityis strong.

Description

technical field [0001] The invention relates to the technical field of tracking, in particular to a method for tracking an object. Background technique [0002] Traditional vision-based target tracking algorithms include motion detection, MeanShift, KCF, TLD, etc. The motion detection algorithm is simple and cannot detect scenes with background changes; MeanShift and KCF have a small amount of calculation, and the tracking effect is good for situations where the target size does not change much. , TLD is complex to calculate. Like KCF, it has requirements for the size change of the target. For fast and small target objects, due to the small size, the features are easily affected by the background, and the tracking effect is poor. KCF and TLD are a kind of The online learning algorithm needs to manually specify the object to be tracked before starting the track. It cannot achieve automatic tracking, and it needs to be re-specified after the target is lost. The practical effec...

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): G06T7/254G06T7/246G06T3/40
CPCG06T3/40G06T2207/10016G06T7/246G06T7/254
Inventor 潘锟张永光汤伟宾沈俊雄杨辉
Owner XIAMEN MEIYA PICO INFORMATION
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