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

Online Multi-target Tracking Method Based on Trajectory Metric Learning

A multi-target tracking and metric learning technology, which is applied in the field of online multi-target tracking, can solve the problems of poor practicability and achieve the effects of good practicability, improved tracking effect, and enhanced resolution ability

Active Publication Date: 2022-05-31
NORTHWESTERN POLYTECHNICAL UNIV
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the shortcomings of poor practicability of existing online multi-target tracking methods, the present invention provides an online multi-target tracking method based on trajectory metric learning

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
  • Online Multi-target Tracking Method Based on Trajectory Metric Learning
  • Online Multi-target Tracking Method Based on Trajectory Metric Learning
  • Online Multi-target Tracking Method Based on Trajectory Metric Learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0051] refer to Figure 1-3 . The specific steps of the online multi-target tracking method based on trajectory metric learning of the present invention are as follows:

[0052] Step 1. Consider the length of the trajectory, the degree of occlusion of the target, the closeness of the trajectory and the detection response, the smoothness of the trajectory and other factors, and design the trajectory confidence function:

[0053]

[0054] in represents the trajectory length of the i-th target up to time t, represents the closeness between the i-th target and its corresponding detection response at time k, Indicates the degree to which the ith target at time k is not occluded, T k is all target trajectories at time k, smo(T t i ) represents the smoothness of the trajectory of the i-th target up to time t.

[0055] Step 2. For the trajectory T of the i-th target at time t t i , use the Kalman filter to estimate the position of the target at time k Considering the ...

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 an online multi-target tracking method based on trajectory metric learning, which is used to solve the technical problem of poor practicability of the existing online multi-target tracking method. The technical solution is to first use the existing target detection algorithm to generate a detection response; then, divide the existing trajectory set into a high confidence set and a low confidence set, and use static features and traditional measurement methods to process the high confidence set and the next moment detection response For the data connection problem, the similarity measure matrix is ​​used to enhance the data connection ability for low confidence sets, and the final result is obtained. The present invention uses the existing trajectory information as a training sample set to learn the similarity measurement matrix between the trajectory and the detection response online, thereby enhancing the discrimination ability of trajectory discrimination. It solves the technical problems that the background technology method is difficult to accurately connect data, and the tracking effect is severely limited by the detection effect, improves the multi-target tracking effect, and has good practicability.

Description

technical field [0001] The invention relates to an online multi-target tracking method, in particular to an online multi-target tracking method based on trajectory metric learning. Background technique [0002] Visual multi-target tracking can match the detection responses at different times one by one given the video sequence and target detection results, and finally obtain the dynamic changes of the target in the temporal and spatial domains. As an important part of visual perception and understanding, multi-target tracking can effectively extract the motion information and temporal change information of targets, and can assist smart devices to accurately and robustly understand the surrounding environment in both the temporal and spatial domains. For example, applying multi-target tracking to an intelligent monitoring system will help detect abnormal targets; applying it to smart cars will help avoid rear-end collisions, collisions and other traffic accidents; applying to...

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 Patents(China)
IPC IPC(8): G06T7/246G06V10/56G06V10/50
Inventor 王琦李学龙张星宇
Owner NORTHWESTERN POLYTECHNICAL UNIV
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