PRiDAN: Person Re-identification from Drones with Adaptive Weights and Expanded Neighbourhood

Chatchanan Varojpipath, Krystian Mikolajczyk

2022

Abstract

There has been a growing interest in drone applications and many computer vision tasks were specifically adapted to drone scenarios such as SLAM, object detection, depth estimation, etc. Person re-identification is one of the tasks that can be effectively performed from drones and new datasets specifically geared towards aerial person imagery emerge. In addition to the common problems found in almost every person re-ID dataset, the most significant difference to static CCTV re-ID is the very different human pose across views from the top and similar appearance of different people but also motion blur, light conditions, low resolution and occlusions. To address these problems, we propose to combine a Part-based Convolutional Baseline (PCB), which exploits local features, with an adaptive weight distribution strategy, which assigns different weights to similar and dissimilar samples. The result shows that our method outperforms the state of the arts by a large margin. In addition, we propose a re-ranking method which aggregates Expanded Cross Neighborhood (ECN) distance and Jaccard distance to compute the final ranking. Compared to the existing methods, our re-ranking achieves 3.30% and 3.03% improvement on mAP and rank-1 accuracy, respectively.

Paper Citation

in Harvard Style

Varojpipath C. and Mikolajczyk K. (2022). PRiDAN: Person Re-identification from Drones with Adaptive Weights and Expanded Neighbourhood. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-549-4, pages 411-422. DOI: 10.5220/0010820000003122

in Bibtex Style

@conference{icpram22,
author={Chatchanan Varojpipath and Krystian Mikolajczyk},
title={PRiDAN: Person Re-identification from Drones with Adaptive Weights and Expanded Neighbourhood},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2022},
pages={411-422},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010820000003122},
isbn={978-989-758-549-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - PRiDAN: Person Re-identification from Drones with Adaptive Weights and Expanded Neighbourhood
SN - 978-989-758-549-4
AU - Varojpipath C.
AU - Mikolajczyk K.
PY - 2022
SP - 411
EP - 422
DO - 10.5220/0010820000003122