Space-Filling Curve-Based Traffic Event Detection Using Deep Learning and Optical Flow - A Conceptual Framework for Efficient Traffic Event Detection in Vehicle-Mounted Video Data

Abstract

Identifying and analyzing traffic events in large-scale, unstructured video data from vehicle-mounted cameras is a significant challenge for enhancing advanced driver assistance systems (ADAS). This thesis presents a conceptual framework that leverages machine learning (ML) and optical flow (OF) for efficient traffic event detection, utilizing space-filling curves (SFCs) to reduce data dimensionality. Our first approach, ML-SFC, uses an ML model predicting human attention to identify events, while the second, OF-SFC, employs an OF algorithm to detect movement. Both methods are evaluated using the synthetic SMIRK dataset and validated on the real-world Zenseact Open Dataset (ZOD). The results show that OF-SFC performs better on the synthetic dataset, while ML-SFC is better on the real-world dataset. Both methods achieve comparable processing speeds, indicating their suitability for real-time applications. This framework could serve as a foundation for scalable solutions to analyze large volumes of unstructured data in the form of traffic event detection or other contexts. The source code for our framework is available here: https://github.com/erikwessman/ted-sfc.