Privacy-Preserving Person Re-Identification from Temporal Sequences with Transformer and Hungarian Optimization

2026-06-22

AI Summary

This work addresses the limitations of conventional RGB-based person re-identification, which suffers from privacy leakage, sensitivity to illumination variations, and poor robustness under occlusion. To overcome these challenges, we propose a novel RGB-D multimodal temporal modeling approach that integrates depth images with a temporal Transformer encoder for the first time. This framework effectively fuses RGB and depth information while preserving privacy and employs the Hungarian algorithm to achieve globally optimal cross-view matching. The model is optimized using batch-hard triplet loss and demonstrates competitive performance on the TVPR2, GODPR, and BIWI RGBD-ID datasets: using only the depth modality, it achieves CMC and mAP scores comparable to state-of-the-art methods, thereby validating both its effectiveness and privacy-preserving nature.

Abstract

Person re-identification (Re-ID) is a crucial task in surveillance and human behavior analysis, often used in public spaces such as transport hubs. Traditional RGB-based Re-ID methods raise privacy concerns and are highly sensitive to lighting variations and occlusion. In this paper, we propose a novel Re-ID approach that leverages depth images, which inherently obscures facial and other identifiable features, making it a privacy-preserving solution. Our method addresses the association problem between multiple views of individuals by applying the Hungarian algorithm, optimizing the matching process through minimization of the global cost across the distance matrix. We further enhance the approach by introducing temporal sequences of frames as input to a Transformer encoder architecture, which exploits both RGB and depth modalities. This architecture captures dynamic movement patterns, improving feature extraction and re-identification accuracy. Additionally, we employ batch hard triplet loss to enhance discriminative feature learning by focusing on the hardest samples. We evaluate both depth-only and RGB-D models on several top-view datasets, including TVPR2, GODPR, and BIWI RGBD-ID. Our results demonstrate that depth-only re-identification can achieve competitive performance compared to state-of-the-art methods, as measured by standard metrics such as Cumulative Matching Characteristics (CMC) and Mean Average Precision (mAP), while prioritizing privacy preservation.

Problem

• Person Re-Identification
• Privacy Preservation
• Depth Images
• Occlusion
• Lighting Variations

Innovation

• privacy-preserving
• depth-based Re-ID
• Transformer encoder
• Hungarian algorithm
• temporal sequences