用视觉语言模型检测分心驾驶，通过去耦合提升准确性
Zero-Shot Distracted Driver Detection via Vision Language Models with Double Decoupling
- 分离驾驶员外貌特征与行为信号，避免误判
- 在真实场景中准确率显著优于现有方法
- 适合道路安全监控系统快速部署
分心驾驶是交通事故的主要原因，亟需鲁棒且可扩展的检测方法。视觉语言模型（VLM）具备强大的零样本图像分类能力，但现有基于VLM的分心驾驶检测器在真实场景中表现不佳。我们发现，个体外观差异（如服装、年龄、性别）是关键瓶颈：VLM将这些因素与行为线索混淆，导致判断依赖于驾驶员身份而非行为。为此，我们提出一种主体解耦框架，先提取驾驶员外观嵌入，并在零样本分类前将其从图像嵌入中移除，从而突出与分心相关的证据。此外，通过施蒂费尔流形上的度量投影对文本嵌入正交化，提升语义可分性同时保持原意。实验表明，该方法持续优于已有基线，展现出在实际道路安全应用中的潜力。代码已开源。
原文摘要 · Abstract (English)
Distracted driving is a major cause of traffic collisions, calling for robust and scalable detection methods. Vision-language models (VLMs) enable strong zero-shot image classification, but existing VLM-based distracted driver detectors often underperform in real-world conditions. We identify subject-specific appearance variations (e.g., clothing, age, and gender) as a key bottleneck: VLMs entangle these factors with behavior cues, leading to decisions driven by who the driver is rather than what the driver is doing. To address this, we propose a subject decoupling framework that extracts a driver appearance embedding and removes its influence from the image embedding prior to zero-shot classification, thereby emphasizing distraction-relevant evidence. We further orthogonalize text embeddings via metric projection onto Stiefel manifold to improve separability while staying close to the original semantics. Experiments demonstrate consistent gains over prior baselines, indicating the promise of our approach for practical road-safety applications. Code is available at https://github.com/mtakamichi/ZVL-DDD
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