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.

使用神经形态视觉传感器和事件相机，结合循环卷积神经网络的座椅安全状态检测算法，在模拟和真实测试集上分别实现了 0.989 和 0.944 的快系 / 解系的 F1 得分，并在判别系带装上 / 解经动作时分别获得了 0.964 和 0.846 的 F1 得分。