PQ-DAF: Pose-driven Quality-controlled Data Augmentation for Data-scarce Driver Distraction Detection

TL;DR

This paper introduces PQ-DAF, a novel data augmentation framework that uses pose-driven synthesis and quality filtering to improve driver distraction detection in data-scarce scenarios, enhancing model robustness across domains.

Contribution

The paper proposes a new pose-driven data augmentation method with quality control, leveraging vision-language models to generate and filter synthetic training data for better generalization.

Findings

Significant performance improvements in few-shot driver distraction detection.

Enhanced cross-domain robustness of the detection models.

Effective filtering of synthetic samples improves dataset reliability.

Abstract

Driver distraction detection is essential for improving traffic safety and reducing road accidents. However, existing models often suffer from degraded generalization when deployed in real-world scenarios. This limitation primarily arises from the few-shot learning challenge caused by the high cost of data annotation in practical environments, as well as the substantial domain shift between training datasets and target deployment conditions. To address these issues, we propose a Pose-driven Quality-controlled Data Augmentation Framework (PQ-DAF) that leverages a vision-language model for sample filtering to cost-effectively expand training data and enhance cross-domain robustness. Specifically, we employ a Progressive Conditional Diffusion Model (PCDMs) to accurately capture key driver pose features and synthesize diverse training examples. A sample quality assessment module, built upon the CogVLM vision-language model, is then introduced to filter out low-quality synthetic samples based on a confidence threshold, ensuring the reliability of the augmented dataset. Extensive experiments demonstrate that PQ-DAF substantially improves performance in few-shot driver distraction detection, achieving significant gains in model generalization under data-scarce conditions.