Traffic Accident Detection Video Dataset for AI-Driven Computer Vision Systems in Smart City Transportation

We introduce a novel dataset consisting of approximately 5,700 video files, specifically designed to enhance the development of real-time traffic accident detection systems in smart city environments. It encompasses a diverse range of traffic scenarios, captured through Traffic/Surveillance Cameras (Trafficam) and Dash Cameras (Dashcam), along with additional external data sources. The dataset is meticulously organized into three segments: Training, Validation, and Testing, with each segment offering a unique blend of traffic and dashcam footage across different scenarios.

The dataset is divided into eight classes: Backend, Backend Rollover, Frontend, Frontend Rollover, No Accident Normal Traffic, Sidehit, Sidehit Rollover, and General Augmented Crash. These classes provide a rich tapestry of real-world situations, ranging from routine traffic conditions to complex accident scenes. The distribution of the dataset is as follows: 3,912 files for Training, 1,054 for Validation, and 725 for Testing, encompassing a mix of accident and normal traffic scenarios from both Trafficam and Dashcam sources, along with additional external data.

The videos have been processed and segmented into five (5) seconds non-overlapping clips to ensure conciseness, focusing on the rapid dynamics of accidents. This careful curation and classification make the dataset an invaluable resource for training and evaluating AI models in traffic safety applications. By providing a wide array of scenarios, this dataset enables researchers and developers to develop state-of-the-art algorithms, ensuring high accuracy and reliability in diverse urban settings. This dataset is crucial for academic research and also serves as a practical tool for improving traffic management and safety in smart cities, contributing significantly to the collaborative efforts in creating safer, more efficient urban environments.