论文标题
自动驾驶汽车的基于计算机视觉的事故检测
Computer Vision based Accident Detection for Autonomous Vehicles
论文作者
论文摘要
已经开发了许多深度学习和基于传感器的模型，以检测使用自动驾驶汽车的潜在事故。但是，自动驾驶汽车需要能够检测其道路上其他车辆之间的事故，并采取适当的行动，例如放慢或停止并告知有关当局。在本文中，我们为自动驾驶汽车提供了一种新颖的支持系统，该系统通过仪表板摄像头检测车辆事故。该系统利用蒙版R-CNN框架进行车辆检测和质心跟踪算法来跟踪检测到的车辆。此外，该框架还计算各种参数，例如速度，加速度和轨迹，以确定任何跟踪车辆之间是否发生了事故。该框架已在仪表板镜头的自定义数据集上进行了测试，并在保持较低的错误警报率的同时达到了高事故检测率。
Numerous Deep Learning and sensor-based models have been developed to detect potential accidents with an autonomous vehicle. However, a self-driving car needs to be able to detect accidents between other vehicles in its path and take appropriate actions such as to slow down or stop and inform the concerned authorities. In this paper, we propose a novel support system for self-driving cars that detects vehicular accidents through a dashboard camera. The system leverages the Mask R-CNN framework for vehicle detection and a centroid tracking algorithm to track the detected vehicle. Additionally, the framework calculates various parameters such as speed, acceleration, and trajectory to determine whether an accident has occurred between any of the tracked vehicles. The framework has been tested on a custom dataset of dashcam footage and achieves a high accident detection rate while maintaining a low false alarm rate.
