PGGANet: Pose Guided Graph Attention Network for Person Re-identification

• URL: http://arxiv.org/abs/2111.14411v1
• Date: Mon, 29 Nov 2021 09:47:39 GMT
• Title: PGGANet: Pose Guided Graph Attention Network for Person Re-identification
• Authors: Zhijun He, Hongbo Zhao, Wenquan Feng
• Abstract summary: Person re-identification (ReID) aims at retrieving a person from images captured by different cameras. It has been proved that using local features together with global feature of person image could help to give robust feature representations for person retrieval. We propose a pose guided graph attention network, a multi-branch architecture consisting of one branch for global feature, one branch for mid-granular body features and one branch for fine-granular key point features.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Person re-identification (ReID) aims at retrieving a person from images captured by different cameras. For deep-learning-based ReID methods, it has been proved that using local features together with global feature of person image could help to give robust feature representations for person retrieval. Human pose information could provide the locations of human skeleton to effectively guide the network to pay more attention on these key areas and could also help to reduce the noise distractions from background or occlusions. However, methods proposed by previous pose-related works might not be able to fully exploit the benefits of pose information and did not take into consideration the different contributions of different local features. In this paper, we propose a pose guided graph attention network, a multi-branch architecture consisting of one branch for global feature, one branch for mid-granular body features and one branch for fine-granular key point features. We use a pre-trained pose estimator to generate the key-point heatmap for local feature learning and carefully design a graph attention convolution layer to re-evaluate the contribution weights of extracted local features by modeling the similarities relations. Experiments results demonstrate the effectiveness of our approach on discriminative feature learning and we show that our model achieves state-of-the-art performances on several mainstream evaluation datasets. We also conduct a plenty of ablation studies and design different kinds of comparison experiments for our network to prove its effectiveness and robustness, including holistic datasets, partial datasets, occluded datasets and cross-domain tests.

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