Abstract
Accident detection using Closed Circuit Television (CCTV) footage is one of the most imperative features for enhancing transport safety and efficient traffic control. To this end, this research addresses the issues of supervised monitoring and data deficiency in accident detection systems by adapting excellent deep learning technologies. The motivation arises from rising statistics in the number of car accidents worldwide; this calls for innovation and the establishment of a smart, efficient and automated way of identifying accidents and calling for help to save lives. Addressing the problem of the scarcity of data, the presented framework joins Generative Adversarial Networks (GANs) for synthesizing data and Convolutional Neural Networks (CNN) for model training. Video frames for accidents and non-accidents are collected from YouTube videos, and we perform resizing, image enhancement and image normalisation pixel range adjustments. Three models are used: CNN, Fine-tuned Convolutional Neural Network (FTCNN) and Vision Transformer (VIT) worked best for detecting accidents from CCTV, obtaining an accuracy rate of 94% and 95%, while the CNN model obtained 88%. Such results show that the proposed framework suits traffic safety applications due to its high real-time accident detection capabilities and broad-scale applicability. This work lays the foundation for intelligent surveillance systems in the future for real-time traffic monitoring, smart city framework, and integration of intelligent surveillance systems into emergency management systems.
Abstract (translated)
使用闭路电视（CCTV）录像进行事故检测是提高交通安全和有效交通控制最重要的特征之一。为此，本研究通过采用先进的深度学习技术来解决事故检测系统中监督监控不足和数据缺乏的问题。这一动机源自全球汽车事故发生率上升的统计数据；这要求创新并建立一种智能、高效且自动化的识别事故并向外界求援的方法以拯救生命。 为了解决数据稀缺问题，所提出的框架结合了生成对抗网络（GANs）用于合成数据以及卷积神经网络（CNN）进行模型训练。从YouTube视频中收集到的交通事故和非交通事故视频帧进行了调整大小、图像增强及像素范围标准化处理。使用了三种模型：CNN、微调后的卷积神经网络（FTCNN）和视觉变换器（VIT），这些模型在识别CCTV中的事故时表现最佳，准确率分别达到了94%和95%，而CNN模型的准确率为88%。这样的结果表明所提出的框架适用于交通安全管理应用，因其具备高实时性事故检测能力和广泛的应用潜力。 这项工作为未来的智能监控系统奠定了基础，这些系统可用于实时交通监控、智慧城市框架以及将智能监控系统整合到紧急管理系统中。
URL
https://arxiv.org/abs/2506.16186
