A Scene-Aware Degradation Universal Re-Identification Framework for Adverse Weather

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Vision-based Re-identification (ReID) is vital for intelligent surveillance.
• Adverse weather conditions like rain and fog degrade visual clarity and identity cues, challenging existing ReID methods.
• CLIP-based ReID models have not fully explored cross-modal alignment under weather distortions.

Purpose of the Study:

• To develop a universal ReID framework robust to diverse adverse-weather degradations.
• To enhance the performance of ReID systems in challenging environmental conditions.
• To address the limitations of current methods in handling co-occurring multiple degradations.

Main Methods:

• Proposed ScA-UniReID, a Scene-Aware Degradation Universal ReID framework utilizing CLIP's dual-encoder architecture.
• Introduced dual textual prompts: target-oriented for identity and degradation-oriented for weather noise.
• Implemented an adaptive control module for dynamic re-weighting to disentangle identity semantics from degradation artifacts.

Main Results:

• ScA-UniReID demonstrated superior performance on pedestrian and maritime ReID benchmarks under various adverse-weather protocols.
• The framework showed robust generalization capabilities to unseen conditions.
• Effectively disentangled identity features from weather-induced artifacts.

Conclusions:

• ScA-UniReID offers a robust and universal solution for vision-based ReID in adverse weather.
• The proposed method significantly advances the state-of-the-art in challenging surveillance scenarios.
• Validates the efficacy of scene-aware, degradation-oriented approaches for cross-modal ReID.