轻量多任务网络实时监测驾驶状态，兼顾低延迟与低算力需求。
Low-Latency Embedded Driver Monitoring System with a Multi-Task Neural Network
- 单次前向传播预测面部多个注意力指标
- 满足实时性要求且适配嵌入式设备算力限制
- 适合车载系统部署，用于疲劳与分心检测
道路交通事故仍是全球重大问题，多数由人为因素如分心或疲劳驾驶导致。本文提出一种基于摄像头的方案，通过分析面部区域提取驾驶员专注度与警觉性的关键指标。所设计的处理流程在满足严苛实时性要求的同时，大幅降低计算开销，可在计算资源受限的嵌入式设备上部署。为此，我们开发了一种轻量级多任务神经网络，在一次前向传播中同时预测多个面部区域指标。该模型被集成至完整执行流程，实现实时评估驾驶员的专注度、疲劳状态及分心行为。
原文摘要 · Abstract (English)
Road traffic accidents remain a significant global concern, with the majority attributed to human factors such as driver distraction and fatigue. This study proposes a camera-based approach to derive useful indicators to assess driver attentiveness and alertness. The proposed pipeline jointly satisfies the stringent real-time requirements imposed by the critical application and minimizes the computational requirements to allow for deployment on a tight computational budget. To this end, we develop a lightweight multi-task neural network that predicts multiple indicators for the face region in a single forward pass. The developed model is integrated into a complete execution workflow to produce a real-time estimate of attentiveness, fatigue, and engagement in distracting activities.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。