论文标题
强大的两流多功能网络用于驱动器嗜睡检测
Robust Two-Stream Multi-Feature Network for Driver Drowsiness Detection
论文作者
论文摘要
嗜睡驾驶是交通事故的主要原因，因此以前的许多研究都集中在驾驶员嗜睡检测上。已经考虑了许多相关因素以进行疲劳检测，并可能导致高精度，但是仍然存在一些严重的限制，例如大多数现有模型在环境上都很容易受到影响。在本文中，疲劳检测被认为是时间动作检测问题，而不是图像分类。提出的检测系统可以分为四个部分：（1）定位检测到的驱动器图片的关键斑块，这对于疲劳检测至关重要并计算相应的光流。 （2）在我们的系统中使用对比度有限的自适应直方图均衡（CLAHE）来减少不同光条件的影响。 （3）为每个功能设计了三个单独的两流网络与注意机制相结合，以提取时间信息。 （4）三个子网络的输出将被串联并发送到完全连接的网络，该网络判断驱动程序的状态。嗜睡检测系统对著名的国家tsing Hua University Driver嗜睡检测（NTHU-DDD）数据集进行了训练和评估，我们获得了94.46％的准确性，这表现优于大多数现有的疲劳检测模型。
Drowsiness driving is a major cause of traffic accidents and thus numerous previous researches have focused on driver drowsiness detection. Many drive relevant factors have been taken into consideration for fatigue detection and can lead to high precision, but there are still several serious constraints, such as most existing models are environmentally susceptible. In this paper, fatigue detection is considered as temporal action detection problem instead of image classification. The proposed detection system can be divided into four parts: (1) Localize the key patches of the detected driver picture which are critical for fatigue detection and calculate the corresponding optical flow. (2) Contrast Limited Adaptive Histogram Equalization (CLAHE) is used in our system to reduce the impact of different light conditions. (3) Three individual two-stream networks combined with attention mechanism are designed for each feature to extract temporal information. (4) The outputs of the three sub-networks will be concatenated and sent to the fully-connected network, which judges the status of the driver. The drowsiness detection system is trained and evaluated on the famous Nation Tsing Hua University Driver Drowsiness Detection (NTHU-DDD) dataset and we obtain an accuracy of 94.46%, which outperforms most existing fatigue detection models.
