Bakheet Samy, Al-Hamadi Ayoub
Faculty of Computers and Artificial Intelligence, Sohag University, P.O. Box 82524, Sohag, Egypt.
Institute for Information Technology and Communications (IIKT) Otto-von-Guericke-University Magdeburg, D-39106 Magdeburg, Germany.
Heliyon. 2022 Nov 3;8(11):e11397. doi: 10.1016/j.heliyon.2022.e11397. eCollection 2022 Nov.
Vehicular accident prediction and detection has recently garnered curiosity and large amounts of attention in machine learning applications and related areas, due to its peculiar and fascinating application potentials in the development of Intelligent Transportation Systems (ITS) that play a pivotal role in the success of emerging smart cities. In this paper, we present a new vision-based framework for real-time vehicular accident prediction and detection, based on motion temporal templates and fuzzy time-slicing. The presented framework proceeds in a stepwise fashion, starting with automatically detecting moving objects (i.e., on-road vehicles or roadside pedestrians), followed by dynamically keep tracking of the detected moving objects based on temporal templates, clustering and supervised learning. Then, an extensive set of local features is extracted from the temporal templates of moving objects. Finally, an effective deep neural network (DNN) model is trained on the extracted features to detect abnormal vehicle behavioral patterns and thus predict an accident just before it occurs. The experiments on real-world vehicular accident videos demonstrate that the framework can yield mostly promising results by achieving a hit rate of 98.5% with a false alarm rate of 4.2% that compare very favorably to those from existing approaches, while still being able to deliver delay guarantees for realtime traffic monitoring and surveillance applications.
由于车辆事故预测与检测在智能交通系统（ITS）开发中具有独特且引人入胜的应用潜力，而智能交通系统在新兴智慧城市的成功中起着关键作用，因此最近在机器学习应用及相关领域引发了人们的好奇并受到大量关注。在本文中，我们提出了一种基于运动时间模板和模糊时间切片的用于实时车辆事故预测与检测的新视觉框架。所提出的框架按步骤进行，首先自动检测移动物体（即道路上的车辆或路边行人），然后基于时间模板、聚类和监督学习动态跟踪检测到的移动物体。接着，从移动物体的时间模板中提取大量局部特征。最后，在提取的特征上训练一个有效的深度神经网络（DNN）模型，以检测异常车辆行为模式，从而在事故发生前进行预测。对真实世界车辆事故视频的实验表明，该框架能取得非常有前景的结果，命中率达到98.5%，误报率为4.2%，与现有方法相比非常有利，同时仍能为实时交通监测和监控应用提供延迟保证。
