# Tran-GCN: A Transformer-Enhanced Graph Convolutional Network for Person Re-Identification in Monitoring Videos

arXiv: https://arxiv.org/abs/2409.09391 · HTML: https://mlsanity.com/paper/2409.09391/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 80 references that are in the map.
- First posted on arXiv 14 Sep 2024.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Person Transfer GAN to Bridge Domain Gap for Person Re-Identification](https://mlsanity.com/paper/1711.08565/) (2017)
- [Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)](https://mlsanity.com/paper/1711.09349/) (2017)
- [Learning Discriminative Features with Multiple Granularities for Person Re-Identification](https://mlsanity.com/paper/1804.01438/) (2018)
- [Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro](https://mlsanity.com/paper/1701.07717/) (2017)
- [Beyond triplet loss: a deep quadruplet network for person re-identification](https://mlsanity.com/paper/1704.01719/) (2017)
- [Human Semantic Parsing for Person Re-identification](https://mlsanity.com/paper/1804.00216/) (2018)
- [Part-Aligned Bilinear Representations for Person Re-identification](https://mlsanity.com/paper/1804.07094/) (2018)
- [Pose-driven Deep Convolutional Model for Person Re-identification](https://mlsanity.com/paper/1709.08325/) (2017)
- [A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking](https://mlsanity.com/paper/1711.10378/) (2017)
- [Pose-Normalized Image Generation for Person Re-identification](https://mlsanity.com/paper/1712.02225/) (2017)
- [Deeply-Learned Part-Aligned Representations for Person Re-Identification](https://mlsanity.com/paper/1707.07256/) (2017)
- [AlignedReID: Surpassing Human-Level Performance in Person Re-Identification](https://mlsanity.com/paper/1711.08184/) (2017)
- [NFormer: Robust Person Re-identification with Neighbor Transformer](https://mlsanity.com/paper/2204.09331/) (2022)
- [Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification](https://mlsanity.com/paper/1904.04975/) (2019)
- [Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification](https://mlsanity.com/paper/1710.06555/) (2017)
- [Pose Invariant Embedding for Deep Person Re-identification](https://mlsanity.com/paper/1701.07732/) (2017)
- [Part-Aware Transformer for Generalizable Person Re-identification](https://mlsanity.com/paper/2308.03322/) (2023)
- [GraphAlign: Enhancing Accurate Feature Alignment by Graph matching for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2310.08261/) (2023)
- [Pedestrian Alignment Network for Large-scale Person Re-identification](https://mlsanity.com/paper/1707.00408/) (2017)
- [GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval](https://mlsanity.com/paper/1709.04329/) (2017)
- [Deep Transfer Learning for Person Re-identification](https://mlsanity.com/paper/1611.05244/) (2016)
- [Deep-Person: Learning Discriminative Deep Features for Person Re-Identification](https://mlsanity.com/paper/1711.10658/) (2017)
- [Visual Knowledge in the Big Model Era: Retrospect and Prospect](https://mlsanity.com/paper/2404.04308/) (2024)
- [Multi-modal Fusion Technology based on Vehicle Information: A Survey](https://mlsanity.com/paper/2211.06080/) (2022)

## Built on by (most cited first, 2 of 2)

- [Exploring Stronger Transformer Representation Learning for Occluded Person Re-Identification](https://mlsanity.com/paper/2410.15613/) (2024)
- [Multi-Grained Vision-Language Alignment for Domain Generalized Person Re-Identification](https://mlsanity.com/paper/2603.14012/) (2026)

## Abstract (arXiv)

Person Re-Identification (Re-ID) has gained popularity in computer vision, enabling cross-camera pedestrian recognition. Although the development of deep learning has provided a robust technical foundation for person Re-ID research, most existing person Re-ID methods overlook the potential relationships among local person features, failing to adequately address the impact of pedestrian pose variations and local body parts occlusion. Therefore, we propose a Transformer-enhanced Graph Convolutional Network (Tran-GCN) model to improve Person Re-Identification performance in monitoring videos. The model comprises four key components: (1) A Pose Estimation Learning branch is utilized to estimate pedestrian pose information and inherent skeletal structure data, extracting pedestrian key point information; (2) A Transformer learning branch learns the global dependencies between fine-grained and semantically meaningful local person features; (3) A Convolution learning branch uses the basic ResNet architecture to extract the person's fine-grained local features; (4) A Graph Convolutional Module (GCM) integrates local feature information, global feature information, and body information for more effective person identification after fusion. Quantitative and qualitative analysis experiments conducted on three different datasets (Market-1501, DukeMTMC-ReID, and MSMT17) demonstrate that the Tran-GCN model can more accurately capture discriminative person features in monitoring videos, significantly improving identification accuracy.