# Group Re-Identification with Multi-grained Matching and Integration

**Authors:** Weiyao Lin, Yuxi Li, Hao Xiao, John See, Junni Zou, Hongkai Xiong,, Jingdong Wang, Tao Mei

arXiv: 1905.07108 · 2019-05-28

## TL;DR

This paper introduces a novel multi-grained representation and dynamic matching approach for robust group re-identification across camera views, addressing challenges like pose, layout, and membership variations.

## Contribution

It proposes a new multi-grained group representation and a multi-order matching method with iterative weight updates for improved group Re-ID accuracy.

## Key findings

- Effective on complex multi-camera datasets
- Outperforms existing group Re-ID methods
- Demonstrates robustness to layout and membership changes

## Abstract

The task of re-identifying groups of people underdifferent camera views is an important yet less-studied problem.Group re-identification (Re-ID) is a very challenging task sinceit is not only adversely affected by common issues in traditionalsingle object Re-ID problems such as viewpoint and human posevariations, but it also suffers from changes in group layout andgroup membership. In this paper, we propose a novel conceptof group granularity by characterizing a group image by multi-grained objects: individual persons and sub-groups of two andthree people within a group. To achieve robust group Re-ID,we first introduce multi-grained representations which can beextracted via the development of two separate schemes, i.e. onewith hand-crafted descriptors and another with deep neuralnetworks. The proposed representation seeks to characterize bothappearance and spatial relations of multi-grained objects, and isfurther equipped with importance weights which capture varia-tions in intra-group dynamics. Optimal group-wise matching isfacilitated by a multi-order matching process which in turn,dynamically updates the importance weights in iterative fashion.We evaluated on three multi-camera group datasets containingcomplex scenarios and large dynamics, with experimental resultsdemonstrating the effectiveness of our approach. The published dataset can be found in \url{http://min.sjtu.edu.cn/lwydemo/GroupReID.html}

## Full text

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## Figures

33 figures with captions in the complete paper: https://tomesphere.com/paper/1905.07108/full.md

## References

43 references — full list in the complete paper: https://tomesphere.com/paper/1905.07108/full.md

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Source: https://tomesphere.com/paper/1905.07108