Xu, Sixuan (Southeast University), ZHANG, Tianran (Southeast University), zhao, lei (Southeast University), Zhou, WeI (Nanjing University of Science and Technology), Nastic, Stefan (TU Wien), Wang, Chen (Southeast University)

Surveillance-Based Spatial Temporal Traffic Accident Detection: A Novel Dataset and Tailored Algorithm

Scheduled for presentation during the Regular Session "S37b-Reliable Perception and Robust Sensing for Intelligent Vehicles" (FR-EA-T37), Friday, November 21, 2025, 13:50−14:10, Coolangata 1

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

Traffic Accident Detection (TAD) in surveillance videos is a critical task in Intelligent Transportation Systems (ITS). However, current TAD does not analyze the fine-grained information of the specific accident, only identifies the existence or occurrence time of traffic accidents in a video. This study presents a novel Dataset named STTAD that covers fine-grained information such as multiple categories and their Spatial Temporal Occurrence Regions in surveillance videos. Moreover, a tailored algorithm named STFN is proposed for the implementation of Event-Level TAD. Experimental results demonstrate that STFN could effectively extract the video features and detect the Spatial Temporal Occurrence Regions of multiple accident categories, but further efforts are indeed needed in Event-Level TAD. The STTAD dataset and the tailored algorithm will be open-sourced for research use available through https://github.com/ZTR02/STTAD.git.