Robust Depth-based Person Re-identification

Abstract

Person re-identification (re-id) aims to match people across non-overlapping camera views. So far the RGB-based appearance is widely used in most existing works. However, when people appeared in extreme illumination or changed clothes, the RGB appearance-based re-id methods tended to fail. To overcome this problem, we propose to exploit depth information to provide more invariant body shape and skeleton information regardless of illumination and color change. More specifically, we exploit depth voxel covariance descriptor and further propose a locally rotation invariant depth shape descriptor called Eigen-depth feature to describe pedestrian body shape. We prove that the distance between any two covariance matrices on the Riemannian manifold is equivalent to the Euclidean distance between the corresponding Eigen-depth features. Furthermore, we propose a kernelized implicit feature transfer scheme to estimate Eigen-depth feature implicitly from RGB image when depth information is not available. We find that combining the estimated depth features with RGB-based appearance features can sometimes help to better reduce visual ambiguities of appearance features caused by illumination and similar clothes. The effectiveness of our models was validated on publicly available depth pedestrian datasets as compared to related methods for person re-identification.

IEEE Transactions on Image Processing Early Access

References in corpus (2)

• Person Re-Identification by Camera Correlation Aware Feature Augmentation
• An Enhanced Deep Feature Representation for Person Re-identification

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• Cross-modality Person re-identification with Shared-Specific Feature Transfer
• Person Recognition in Personal Photo Collections
• Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification
• A Deep Structure of Person Re-Identification using Multi-Level Gaussian Models
• Apparel-invariant Feature Learning for Apparel-changed Person Re-identification
• SM-SGE: A Self-Supervised Multi-Scale Skeleton Graph Encoding Framework for Person Re-Identification
• HPILN: A feature learning framework for cross-modality person re-identification
• Privacy-Preserving Person Re-Identification from Temporal Sequences with Transformer and Hungarian Optimization