Pedestrian Re-Identification Using Virtual Samples
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Solution Overview
Problem
Existing pedestrian re-identification methods face challenges in privacy protection due to the need for large datasets of pedestrian images, which can expose personal information, and struggle with domain gaps between virtual and real images, leading to poor robustness and accuracy.
Innovation Solution
A privacy-protected pedestrian re-identification method using virtual samples generated by a game engine, processed through a multi-factor variational generation network to fuse virtual persons with real backgrounds and poses, adjusting resolution and lighting conditions, and sampling based on target dataset attributes, to create a training dataset for a re-identification model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real pedestrian images are used for training re-identification models, then model performance and accuracy are improved, but privacy leakage and security issues occur
Solution Approach 1:
The patent creates virtual pedestrian samples that copy the essential visual characteristics and appearance features of real pedestrians without using actual personal images. These virtual samples serve as substitutes for real pedestrian data in training re-identification models, maintaining model performance while eliminating privacy risks associated with using real person images
Solution Approach 2:
The patent introduces virtual pedestrian samples as an intermediary between real pedestrian data and the re-identification model training process. This intermediary layer allows the model to learn from realistic pedestrian appearances without direct exposure to sensitive real-person images, thus bridging the gap between privacy protection and model effectiveness
2Object-affected harmful factors
If virtual samples are used to train re-identification models, then privacy protection is achieved, but domain gap between virtual and real images causes poor model robustness
Solution Approach 1:
The patent applies domain adaptation techniques that dynamically adjust model parameters and features to bridge the domain gap between virtual and real images. By learning domain-invariant representations and adapting to target domain characteristics, the model maintains robustness and generalization performance when deployed on real pedestrian images despite being trained primarily on virtual samples
Solution Approach 2:
The patent creates a composite training approach that combines virtual pedestrian samples with domain adaptation mechanisms. This composite strategy integrates the privacy-protection benefits of virtual samples with the robustness requirements of real-world deployment through techniques like adversarial training and feature alignment that merge characteristics from both virtual and real domains
3Object-affected harmful factors
If virtual samples are used to train re-identification models, then privacy protection is achieved, but missing pedestrian appearance information in target images reduces identification accuracy
Solution Approach 1:
The patent creates virtual pedestrian samples that carefully copy and preserve critical appearance features such as clothing patterns, colors, and textures that are essential for re-identification. These virtual samples maintain the discriminative visual characteristics needed for accurate matching while using synthetic personas that protect real individual identities
Solution Approach 2:
The patent employs domain adaptation techniques that adjust model parameters to account for differences between virtual training samples and real target images. This includes learning domain-invariant features and adapting to target domain statistics, enabling the model to maintain high identification accuracy despite the virtual-to-real domain transition
Data Source
AI summary
This invention proposes a pedestrian re-identification method based on virtual samples, comprising following steps: s1) obtaining virtual persons generated by game engine, and generating the virtual samples with person labels by fusing a background of a target dataset and a pose of real persons through a multi-factor variational generation network; s2) rendering the generated virtual samples according to lighting conditions; s3) sampling the rendered virtual samples according to person attributes of target dataset; s4) constructing a training dataset according to virtual samples obtained by sampling to train a pedestrian re-identification model, and verifying identification effect of the trained model. The present invention uses a virtual image generation framework that integrates translation-rendering-sampling to narrow the distribution between virtual images and real images as much as possible to generate virtual samples, and conduct person re-identification model training, which can be effectively and effectively applied to pedestrian datasets in real scenes.