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

A method and system for re-identification of people changing clothes based on autoencoder network

A self-encoding network and pedestrian re-identification technology, which is applied in the field of pedestrian re-identification methods and systems for changing clothes, can solve the problems of lack of training data, unsatisfactory effects, and hinder the commercialization of pedestrian re-identification methods, and achieve the effect of good model performance.

Active Publication Date: 2021-08-03
ZHEJIANG UNIV
View PDF6 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

These methods perform well in short-term person re-identification tasks, but do not perform well when applied to long-term person re-id scenarios with drastic clothing changes.
[0008] From the above two points, it can be concluded that there are currently two other key issues that hinder the commercialization of pedestrian re-identification methods: 1. Lack of labeled training data with a large number of pedestrian representation changes; 2. Lack of a method that is robust to pedestrian representation changes. The feature learning method of

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
  • A method and system for re-identification of people changing clothes based on autoencoder network
  • A method and system for re-identification of people changing clothes based on autoencoder network
  • A method and system for re-identification of people changing clothes based on autoencoder network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0052] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be noted that the following embodiments are intended to facilitate the understanding of the present invention, but do not limit it in any way.

[0053] Such as figure 1 As shown, a re-identification method based on autoencoder network for pedestrians changing clothes, including the following steps:

[0054] S01, using the pedestrian part parser to generate a mask for the pedestrian's clothing part in the pedestrian picture;

[0055] S02, extract the clothing part in the pedestrian picture according to the mask, and use a trained clothing feature encoder E A Get the clothes feature representation vector;

[0056] S03, remove the clothing part in the pedestrian picture according to the mask, use the trained clothes changing picture generator G, use the clothes feature representation vector and the pedestrian picture with the clothes remo...

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 method and system for re-identifying pedestrians who change clothes based on an autoencoder network, wherein the method includes: (1) generating a mask for the pedestrian's clothing part in the pedestrian picture; (2) using a clothing feature encoder E A Obtain the clothes feature representation vector; (3) remove the clothes part in the pedestrian picture according to the mask, and use the clothes changing picture generator G to generate the clothes changing picture; (4) build a clothes-independent feature learning network, including the feature extractor F and the picture generation (5) pair the real pedestrian pictures and the generated clothes-changing pictures in pairs, and train the feature extractor F; (6) after the training converges, input the real and marked pedestrian pictures, and use the cross-entropy loss function to The feature extractor F is fine-tuned; (7) The application of pedestrian re-identification is carried out by using the final feature extractor F. Utilizing the present invention, robust discriminative features can be learned in the scene where the appearance of pedestrians varies widely.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a method and system for re-identifying pedestrians changing clothes based on an autoencoder network. Background technique [0002] The problem of pedestrian re-identification aims to use the target person's photo as input to retrieve the historical records of the target person taken at other time points and under different cameras. Due to its wide range of applications, such as unmanned supermarkets, target person tracking, crime prevention, search for lost elderly and children, target person activity analysis, etc., the pedestrian re-identification system has rich application scenarios in real life. Therefore, the problem of person re-identification has attracted extensive attention in the field of computer vision in recent years. [0003] The problem of pedestrian re-identification is challenging, mainly due to the drastic lighting changes, pedestrian posture changes, camera ang...

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): G06K9/00G06K9/62G06N3/04
CPCG06V40/10G06N3/045G06F18/21G06F18/22G06F18/214
Inventor 余正旭蔡登金仲明洪斌黄建强华先胜
Owner ZHEJIANG 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