Graph(Graph): A Nested Graph-Based Framework for Early Accident Anticipation

Nupur Thakur, PrasanthSai Gouripeddi, Baoxin Li; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 7533-7541

Anticipating traffic accidents early using dashcam videos is an important task for ensuring road safety and building reliable intelligent autonomous vehicles. However, factors like high traffic on the roads, different types of accidents, limited angles of vision, etc. make this task very challenging. Using the early frames, a lot of existing methods predict a large number of false positives which poses a huge risk for all vehicles on the road. In this paper, we propose a novel end-to-end learning, nested graph-based framework named Graph(Graph) for early accident anticipation. It uses interactions between the objects in the same as well as the neighboring frames along with the global features to make precise predictions as early as possible. This way it is able to embed the local as well as global temporal information into the extracted features. Graph(Graph) outperforms state-of-the-art methods on different datasets by a large margin demonstrating its effectiveness. With empirical evidence, we highlight the importance of each component in Graph(Graph) and show their effect on the final performance. Our code is available at https://github.com/thakurnupur/Graph-Graph.