# The Research of Group Re-identification from Multiple Cameras

arXiv: https://arxiv.org/abs/2407.14620 · HTML: https://mlsanity.com/paper/2407.14620/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 54 references that are in the map.
- First posted on arXiv 19 Jul 2024.

## Builds on (most cited first, 17 of 17)

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [Person Re-identification in the Wild](https://mlsanity.com/paper/1604.02531/) (2016)
- [End-to-end people detection in crowded scenes](https://mlsanity.com/paper/1506.04878/) (2015)
- [Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification](https://mlsanity.com/paper/1604.07528/) (2016)
- [IAN: The Individual Aggregation Network for Person Search](https://mlsanity.com/paper/1705.05552/) (2017)
- [Deep Feature Learning with Relative Distance Comparison for Person Re-identification](https://mlsanity.com/paper/1512.03622/) (2015)
- [Learning a Discriminative Null Space for Person Re-identification](https://mlsanity.com/paper/1603.02139/) (2016)
- [Person Re-identification by Saliency Learning](https://mlsanity.com/paper/1412.1908/) (2014)
- [MGTR: Multi-Granular Transformer for Motion Prediction with LiDAR](https://mlsanity.com/paper/2312.02409/) (2023)
- [Group Re-Identification with Multi-grained Matching and Integration](https://mlsanity.com/paper/1905.07108/) (2019)
- [Person Re-identification with Correspondence Structure Learning](https://mlsanity.com/paper/1504.06243/) (2015)
- [Learning Invariant Color Features for Person Re-Identification](https://mlsanity.com/paper/1410.1035/) (2014)
- [SAPI: Surroundings-Aware Vehicle Trajectory Prediction at Intersections](https://mlsanity.com/paper/2306.01812/) (2023)
- [DuoSpaceNet: Leveraging Both Bird's-Eye-View and Perspective View Representations for 3D Object Detection](https://mlsanity.com/paper/2405.10577/) (2024)
- [Human Re-identification by Matching Compositional Template with Cluster Sampling](https://mlsanity.com/paper/1502.00256/) (2015)

## Abstract (arXiv)

Object re-identification is of increasing importance in visual surveillance. Most existing works focus on re-identify individual from multiple cameras while the application of group re-identification (Re-ID) is rarely discussed. We redefine Group Re-identification as a process which includes pedestrian detection, feature extraction, graph model construction, and graph matching. Group re-identification is very challenging since it is not only interfered by view-point and human pose variations in the traditional re-identification tasks, but also suffered from the challenges in group layout change and group member variation. To address the above challenges, this paper introduces a novel approach which leverages the multi-granularity information inside groups to facilitate group re-identification. We first introduce a multi-granularity Re-ID process, which derives features for multi-granularity objects (people/people-subgroups) in a group and iteratively evaluates their importances during group Re-ID, so as to handle group-wise misalignments due to viewpoint change and group dynamics. We further introduce a multi-order matching scheme. It adaptively selects representative people/people-subgroups in each group and integrates the multi-granularity information from these people/people-subgroups to obtain group-wise matching, hence achieving a more reliable matching score between groups. Experimental results on various datasets demonstrate the effectiveness of our approach.