Aufschläger, Robert (Deggendorf Institute of Technology), Shoeb, Youssef Omar (Technical University of Berlin, Continental AG), Nowzad, Azarm (Continental), Heigl, Michael (Deggendorf Institute of Technology), Bally, Fabian (Deggendorf Institute of Technology), Schramm, Martin Michael (Deggendorf Institute of Technology)

Following the Clues: Experiments on Person Re-ID Using Cross-Modal Intelligence

Scheduled for presentation during the Regular Session "S06b-Safety, Sensing, and Infrastructure Design for Vulnerable Road Users" (WE-EA-T6), Wednesday, November 19, 2025, 14:50−14:50, Surfers Paradise 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 19, 2025

Abstract

The collection and release of street-level recordings as Open Data play a vital role in advancing autonomous driving systems and AI research. However, these datasets pose significant privacy risks, particularly for pedestrians, due to the presence of Personally Identifiable Information (PII) that extends beyond biometric traits such as faces. In this paper, we present cRID, a novel cross-modal framework combining Large Vision-Language Models, Graph Attention Networks, and representation learning to detect textual describable clues of PII and enhance person re-identification (Re-ID). Our approach focuses on identifying and leveraging interpretable features, enabling the detection of semantically meaningful PII beyond low-level appearance cues. We conduct a systematic evaluation of PII presence in person image datasets. Our experiments show improved performance in practical cross-dataset Re-ID scenarios, notably from Market-1501 to CUHK03-np (detected), highlighting the framework’s practical utility. Code is available at https://github.com/RAufschlaeger/cRID.