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Person re-identification via semi-supervised adaptive graph embedding.

Jiao Liu^1,2, Mingquan Lin², Mingbo Zhao^2,3

¹Shanghai University of Engineering Science, Shanghai, China.

Applied Intelligence (Dordrecht, Netherlands)

|May 17, 2022

Summary

This summary is machine-generated.

This study introduces a novel scalable manifold embedding method for Person Re-Identification (Re-ID) in smart cities. The approach enhances pedestrian tracking by integrating graph construction and subspace learning for improved accuracy and scalability.

Keywords:

Adaptive graph embedding Dimensionality reduction Semi-supervised learning

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Person Re-Identification (Re-ID) is crucial for smart city public safety using video surveillance.
Existing Re-ID methods often overlook unlabeled gallery images and face scalability issues with Manifold Embedding (ME).

Purpose of the Study:

To propose a novel, scalable manifold embedding approach for Person Re-ID.
To address the limitations of existing methods in utilizing unlabeled data and computational complexity.

Main Methods:

Developed a discriminative and doubly-stochastic graph for enhanced clustering and robustness.
Integrated graph construction and subspace learning within a unified loss framework.
Enabled iterative refinement where subspace results inform graph construction and vice versa.

Main Results:

Achieved superior ranking performance on three benchmark Person Re-ID datasets.
Demonstrated improved clustering performance by considering side information in graph construction.
Showcased robustness and reduced parameter sensitivity due to the doubly-stochastic graph property.

Conclusions:

The proposed scalable manifold embedding method significantly enhances Person Re-ID performance.
The unified framework effectively leverages unlabeled data and improves computational scalability.
This approach offers a robust and efficient solution for pedestrian tracking in smart city surveillance.