Neuromorphic vision sensors, or event cameras, differ from conventional cameras in that they do not capture images at a specified rate. Instead, they asynchronously log local brightness changes at each pixel. As a result, event cameras only record changes in a given scene, and do so with very high temporal resolution, high dynamic range, and low power requirements. Recent research has demonstrated how these characteristics make event cameras extremely practical sensors in driver monitoring systems (DMS), enabling the tracking of high-speed eye motion and blinks. This research provides a proof of concept to expand event-based DMS techniques to include seatbelt state detection. Using an event simulator, a dataset of 108,691 synthetic neuromorphic frames of car occupants was generated from a near-infrared (NIR) dataset, and split into training, validation, and test sets for a seatbelt state detection algorithm based on a recurrent convolutional neural network (CNN). In addition, a smaller set of real event data was collected and reserved for testing. In a binary classification task, the fastened/unfastened frames were identified with an F1 score of 0.989 and 0.944 on the simulated and real test sets respectively. When the problem extended to also classify the action of fastening/unfastening the seatbelt, respective F1 scores of 0.964 and 0.846 were achieved.

翻译：神经形态视觉传感器（即事件相机）与传统相机的区别在于：它不按固定帧率采集图像，而是异步记录每个像素点的局部亮度变化。因此，事件相机仅捕捉场景中的动态变化，并具备极高的时间分辨率、高动态范围和低功耗特性。近年研究表明，这些特性使得事件相机在驾驶员监控系统中极具实用价值，可实现高速眼动和眨眼追踪。本研究提出概念验证，将基于事件相机的驾驶员监控技术扩展至安全带状态检测领域。通过事件模拟器，从近红外数据集中生成108,691帧合成神经形态图像，构建包含训练集、验证集和测试集的数据集，用于基于递归卷积神经网络的检测算法。此外，还采集少量真实事件数据进行测试。在二分类任务中，模拟测试集和真实测试集的系紧/未系紧安全带帧识别F1分数分别达0.989和0.944；当问题扩展至同时分类系紧/解开安全带动作时，对应F1分数分别为0.964和0.846。