# Person Re-identification based on Robust Features in Open-world

arXiv: https://arxiv.org/abs/2102.10798 · HTML: https://mlsanity.com/paper/2102.10798/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 22 Feb 2021.

## Builds on (most cited first, 16 of 16)

- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro](https://mlsanity.com/paper/1701.07717/) (2017)
- [Joint Discriminative and Generative Learning for Person Re-identification](https://mlsanity.com/paper/1904.07223/) (2019)
- [Person Re-identification by Contour Sketch under Moderate Clothing Change](https://mlsanity.com/paper/2002.02295/) (2020)
- [Part-Aligned Bilinear Representations for Person Re-identification](https://mlsanity.com/paper/1804.07094/) (2018)
- [When Person Re-identification Meets Changing Clothes](https://mlsanity.com/paper/2003.04070/) (2020)
- [Spatial and Temporal Mutual Promotion for Video-based Person Re-identification](https://mlsanity.com/paper/1812.10305/) (2018)
- [Revisiting Temporal Modeling for Video-based Person ReID](https://mlsanity.com/paper/1805.02104/) (2018)
- [Learning Longterm Representations for Person Re-Identification Using Radio Signals](https://mlsanity.com/paper/2004.01091/) (2020)
- [Spatially and Temporally Efficient Non-local Attention Network for Video-based Person Re-Identification](https://mlsanity.com/paper/1908.01683/) (2019)
- [Robust Depth-based Person Re-identification](https://mlsanity.com/paper/1703.09474/) (2017)
- [Recurrent Attention Models for Depth-Based Person Identification](https://mlsanity.com/paper/1611.07212/) (2016)
- [Learning Shape Representations for Clothing Variations in Person Re-Identification](https://mlsanity.com/paper/2003.07340/) (2020)
- [AlignGAN: Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1707.01400/) (2017)

## Abstract (arXiv)

Deep learning technology promotes the rapid development of person re-identifica-tion (re-ID). However, some challenges are still existing in the open-world. First, the existing re-ID research usually assumes only one factor variable (view, clothing, pedestrian pose, pedestrian occlusion, image resolution, RGB/IR modality) changing, ignoring the complexity of multi-factor variables in the open-world. Second, the existing re-ID methods are over depend on clothing color and other apparent features of pedestrian, which are easily disguised or changed. In addition, the lack of benchmark datasets containing multi-factor variables is also hindering the practically application of re-ID in the open-world. In this paper, we propose a low-cost and high-efficiency method to solve shortcomings of the existing re-ID research, such as unreliable feature selection, low efficiency of feature extraction, single research variable, etc. Our approach based on pose estimation model improved by group convolution to obtain the continuous key points of pedestrian, and utilize dynamic time warping (DTW) to measure the similarity of features between different pedestrians. At the same time, to verify the effectiveness of our method, we provide a miniature dataset which is closer to the real world and includes pedestrian changing clothes and cross-modality factor variables fusion. Extensive experiments are conducted and the results show that our method achieves Rank-1: 60.9%, Rank-5: 78.1%, and mAP: 49.2% on this dataset, which exceeds most existing state-of-art re-ID models.