Learning Shape Representations for Clothing Variations in Person Re-Identification

Abstract

Person re-identification (re-ID) aims to recognize instances of the same person contained in multiple images taken across different cameras. Existing methods for re-ID tend to rely heavily on the assumption that both query and gallery images of the same person have the same clothing. Unfortunately, this assumption may not hold for datasets captured over long periods of time (e.g., weeks, months or years). To tackle the re-ID problem in the context of clothing changes, we propose a novel representation learning model which is able to generate a body shape feature representation without being affected by clothing color or patterns. We call our model the Color Agnostic Shape Extraction Network (CASE-Net). CASE-Net learns a representation of identity that depends only on body shape via adversarial learning and feature disentanglement. Due to the lack of large-scale re-ID datasets which contain clothing changes for the same person, we propose two synthetic datasets for evaluation. We create a rendered dataset SMPL-reID with different clothes patterns and a synthesized dataset Div-Market with different clothing color to simulate two types of clothing changes. The quantitative and qualitative results across 5 datasets (SMPL-reID, Div-Market, two benchmark re-ID datasets, a cross-modality re-ID dataset) confirm the robustness and superiority of our approach against several state-of-the-art approaches

arXiv comment11 pages, 8 figures. In submission

References in corpus (15)

• In Defense of the Triplet Loss for Person Re-Identification
• Person Re-identification: Past, Present and Future
• Improving Person Re-identification by Attribute and Identity Learning
• A Discriminatively Learned CNN Embedding for Person Re-identification
• Deep Representation Learning with Part Loss for Person Re-Identification
• GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval
• FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification
• Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
• Harmonious Attention Network for Person Re-Identification
• SVDNet for Pedestrian Retrieval
• Joint Discriminative and Generative Learning for Person Re-identification
• RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment
• Deep Group-shuffling Random Walk for Person Re-identification
• Person Re-identification for Real-world Surveillance Systems
• Recover and Identify: A Generative Dual Model for Cross-Resolution Person Re-Identification

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• Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization
• Multi-Object Tracking with Deep Learning Ensemble for Unmanned Aerial System Applications
• When We First Met: Visual-Inertial Person Localization for Co-Robot Rendezvous
• Person Re-identification based on Robust Features in Open-world