Privacy-Preserving Dataset Curation for Kuala Lumpur Urban Traffic: Grounded Vision-Language Detection with Spatial Vehicle-Context Filtering

2026-08-12

AI Summary

This study addresses the high privacy risks inherent in complex tropical traffic scenarios in Kuala Lumpur by proposing an automated anonymization framework. By integrating Grounding DINO with a novel spatial vehicle ROI constraint mechanism, temporal persistence, and automated quality inspection, the method effectively suppresses environmental false positives and enhances occluded object recognition while preserving scene context. Experimental evaluation on a 1,266-frame test set demonstrates an anonymization success rate of approximately 95%, significantly mitigating privacy leakage risks. Consequently, this approach provides a robust solution for the efficient de-identification of urban traffic datasets in tropical environments, balancing data utility with stringent privacy protection requirements.

Abstract

The rapid advancement of intelligent transportation systems and autonomous driving relies heavily on multi-modal urban traffic datasets. However, curating high-fidelity video imagery in complex tropical urban environments---specifically Kuala Lumpur, Malaysia---presents severe challenges for Personally Identifiable Information (PII) anonymization due to high motorcycle density, dark acrylic license plates, dynamic camera tilt, and extreme tropical glare. We propose an automated anonymization framework tailored for the Kuala Lumpur Road Dataset, captured via a mobile cycling platform at 2 FPS. We document how legacy Haar cascades and YOLOv8 fail under these conditions---generating false positives on background elements while missing rotated or occluded targets. Our architecture resolves this by integrating Grounding DINO---a zero-shot open-set vision-language transformer---with a novel Spatial Vehicle Region of Interest (ROI) Containment Engine. By requiring license plate centroids to reside within validated vehicle boundaries, the pipeline suppresses environmental false positives while automatically obfuscating faces, heads, and license plates. An initial evaluation on 1,266 frames demonstrates a $\sim$95\% success rate, with remaining failures restricted to small, heavily occluded, oblique, or ambiguous targets. Coupled with temporal persistence mechanisms and an automated quality-control auditor, the framework minimizes privacy-related false negatives while preserving scene context for downstream vision tasks. While formal legal compliance depends on broader governance procedures, this publicly available pipeline and demonstration notebook provide an auditable preprocessing stage for privacy-aware dataset curation.

Problem

• Privacy-Preserving Dataset Curation
• PII Anonymization
• Urban Traffic
• Kuala Lumpur
• Intelligent Transportation Systems

Innovation

• Grounding DINO
• Spatial Vehicle ROI Containment Engine
• Privacy-Preserving Dataset Curation
• Zero-shot Vision-Language Transformer
• Automated Quality-Control Auditor