论文标题
使用卷积神经网络预测人行横道行为
Predicting Pedestrian Crosswalk Behavior Using Convolutional Neural Networks
论文作者
论文摘要
一个常见但潜在的危险任务是穿越街道的行为。行人的事故对大量年度交通伤亡数量贡献很大，这就是为什么行人使用人行横道等安全措施至关重要的原因。但是，人们常常忘记激活人行横道或无法这样做，例如那些视力障碍或占用手的人。其他行人只是粗心大意，发现人行横道标志着麻烦，这可能导致汽车撞到他们的事故。在本文中，我们通过设计可以自动检测行人并触发人行横道信号的系统来考虑对人行横道系统的改进。我们收集了一个图像数据集，然后使用这些数据集来训练卷积神经网络，以区分行人（包括自行车骑手）和各种虚假警报。所得系统可以实时捕获和评估图像，结果可自动激活系统的人行横道光。在对我们系统在现实世界环境中进行了广泛测试之后，我们得出结论，它是可以作为备用系统可行的，可以补充现有的人行横道按钮，从而提高了越过街道的整体安全性。
A common yet potentially dangerous task is the act of crossing the street. Pedestrian accidents contribute a significant amount to the high number of annual traffic casualties, which is why it is crucial for pedestrians to use safety measures such as a crosswalk. However, people often forget to activate a crosswalk light or are unable to do so -- such as those who are visually impaired or have occupied hands. Other pedestrians are simply careless and find the crosswalk signals a hassle, which can result in an accident where a car hits them. In this paper, we consider an improvement to the crosswalk system by designing a system that can detect pedestrians and triggering the crosswalk signal automatically. We collect a dataset of images that we then use to train a convolutional neural network to distinguish between pedestrians (including bicycle riders) and various false alarms. The resulting system can capture and evaluate images in real time, and the result can be used to automatically activate systems a crosswalk light. After extensive testing of our system in real-world environments, we conclude that it is feasible as a back-up system that can compliment existing crosswalk buttons, and thereby improve the overall safety of crossing the street.
