EPAN: Robust Pedestrian Re-Identification via Enhanced Alignment Network for IoT Surveillance

2025-11-03

AI Summary

To address performance degradation in pedestrian re-identification (ReID) for IoT-based intelligent surveillance—caused by viewpoint variations and environmental interference—this paper proposes the Enhanced Pedestrian Alignment Network (EPAN). EPAN employs a dual-branch architecture integrating multi-scale feature extraction, viewpoint-invariant representation learning, and fine-grained feature alignment to achieve robust cross-camera person representation. Its end-to-end training strategy explicitly mitigates the adverse effects of viewpoint discrepancy and background clutter, significantly enhancing both feature consistency and discriminability. Evaluated on the real-world Inspection-Personnel dataset—specifically constructed for IoT surveillance scenarios—EPAN achieves 90.09% Rank-1 accuracy and 78.82% mean Average Precision (mAP), demonstrating superior effectiveness and practicality in complex, resource-constrained IoT monitoring environments.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Image and Video Retrieval
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Person re-identification (ReID) plays a pivotal role in computer vision, particularly in surveillance and security applications within IoT-enabled smart environments. This study introduces the Enhanced Pedestrian Alignment Network (EPAN), tailored for robust ReID across diverse IoT surveillance conditions. EPAN employs a dual-branch architecture to mitigate the impact of perspective and environmental changes, extracting alignment information under varying scales and viewpoints. Here, we demonstrate EPAN's strong feature extraction capabilities, achieving outstanding performance on the Inspection-Personnel dataset with a Rank-1 accuracy of 90.09% and a mean Average Precision (mAP) of 78.82%. This highlights EPAN's potential for real-world IoT applications, enabling effective and reliable person ReID across diverse cameras in surveillance and security systems. The code and data are available at: https://github.com/ggboy2580/EPAN

Problem

• Enhances pedestrian re-identification accuracy across IoT surveillance systems
• Mitigates perspective and environmental variations in cross-camera tracking
• Improves alignment under diverse scales and viewpoints for robust identification

Innovation

• Dual-branch architecture for robust alignment
• Enhanced feature extraction across varying scales
• Improved pedestrian re-identification in IoT surveillance