In the evolving landscape of traffic management and autonomous driving technology, the analysis of traffic scenes from video data stands as a crucial challenge. Traditional approaches often rely on complex, high-dimensional image analysis, necessitating significant computational resources and sophisticated algorithms. Recognizing the limitations of these methods, our research introduces a novel, streamlined approach centered around a graph-based framework for understanding traffic dynamics.
Central to our methodology is the exploration of complex scene analysis through the lens of object-object interaction within traffic scenes. This interaction dynamics is adeptly captured through our specially designed graph structures, which are further analyzed and interpreted using Graph Neural Networks (GNNs) as a foundational element. By employing GNNs, our framework delves into the intricate dynamics of traffic environments. We focus on the high-level interactions and behaviours within traffic scenes, distilling the essential patterns of movement and relationships among elements such as vehicles and pedestrians.
To validate the effectiveness of our framework, we conducted extensive testing using two prominent datasets: the METEOR Dataset and the INTERACTION Dataset. Our methodology demonstrated exceptional performance, achieving an accuracy of 62.03% on the METEOR Dataset and an impressive 98.50% on the INTERACTION Dataset. These results underscore the capability of our graph-based approach to accurately interpret and analyze the dynamics of traffic scenes.
Through this rigorous evaluation, our research not only showcases the significant advantages of incorporating graph neural networks for traffic scene analysis but also highlights the power of our novel approach in abstracting and understanding the complex patterns of movement and interactions within traffic environments. Our work sets a new benchmark in the field, offering a promising direction for future advancements in traffic management and autonomous vehicle technologies.