Department of Information Technology, University of Sialkot, Sialkot 51040, Pakistan.
School of Physic Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK.
Sensors (Basel). 2022 Aug 31;22(17):6563. doi: 10.3390/s22176563.
In the recent past, a huge number of cameras have been placed in a variety of public and private areas for the purposes of surveillance, the monitoring of abnormal human actions, and traffic surveillance. The detection and recognition of abnormal activity in a real-world environment is a big challenge, as there can be many types of alarming and abnormal activities, such as theft, violence, and accidents. This research deals with accidents in traffic videos. In the modern world, video traffic surveillance cameras (VTSS) are used for traffic surveillance and monitoring. As the population is increasing drastically, the likelihood of accidents is also increasing. The VTSS is used to detect abnormal events or incidents regarding traffic on different roads and highways, such as traffic jams, traffic congestion, and vehicle accidents. Mostly in accidents, people are helpless and some die due to the unavailability of emergency treatment on long highways and those places that are far from cities. This research proposes a methodology for detecting accidents automatically through surveillance videos. A review of the literature suggests that convolutional neural networks (CNNs), which are a specialized deep learning approach pioneered to work with grid-like data, are effective in image and video analysis. This research uses CNNs to find anomalies (accidents) from videos captured by the VTSS and implement a rolling prediction algorithm to achieve high accuracy. In the training of the CNN model, a vehicle accident image dataset (VAID), composed of images with anomalies, was constructed and used. For testing the proposed methodology, the trained CNN model was checked on multiple videos, and the results were collected and analyzed. The results of this research show the successful detection of traffic accident events with an accuracy of 82% in the traffic surveillance system videos.
在最近的过去，为了监控、监测异常人类活动和交通监控的目的，已经在各种公共和私人区域放置了大量的摄像机。在真实环境中检测和识别异常活动是一个巨大的挑战，因为可能存在许多类型的报警和异常活动，如盗窃、暴力和事故。这项研究涉及交通视频中的事故。在现代世界中，视频交通监控摄像机 (VTSS) 用于交通监控和监测。随着人口的急剧增加，事故的可能性也在增加。VTSS 用于检测不同道路和高速公路上的交通异常事件或事故，如交通堵塞、交通拥堵和车辆事故。在大多数事故中，人们都很无助，有些人由于在长高速公路和远离城市的地方无法获得紧急治疗而死亡。本研究提出了一种通过监控视频自动检测事故的方法。文献综述表明，卷积神经网络（CNN）是一种专门的深度学习方法，专门用于处理网格状数据，在图像和视频分析中非常有效。本研究使用 CNN 从 VTSS 捕获的视频中发现异常（事故），并实现滚动预测算法以实现高精度。在 CNN 模型的训练中，构建并使用了包含异常的车辆事故图像数据集（VAID）。为了测试所提出的方法，在多个视频上检查了经过训练的 CNN 模型，并收集和分析了结果。该研究的结果表明，在交通监控系统视频中，成功地检测到了交通事故事件，准确率为 82%。
