Traffic Danger Recognition With Surveillance Cameras Without Training Data
This addresses the need for automated traffic safety monitoring in surveillance systems, offering a novel approach that eliminates the requirement for crash-specific training data.
The paper tackles the problem of automatically identifying and predicting car crashes from arbitrary traffic surveillance cameras without labeled training data, achieving mean errors of 1.80% for distance measurement, 2.77 km/h for speed measurement, 0.24 m for car position prediction, and 2.53 km/h for speed prediction on the BrnoCompSpeed dataset.
We propose a traffic danger recognition model that works with arbitrary traffic surveillance cameras to identify and predict car crashes. There are too many cameras to monitor manually. Therefore, we developed a model to predict and identify car crashes from surveillance cameras based on a 3D reconstruction of the road plane and prediction of trajectories. For normal traffic, it supports real-time proactive safety checks of speeds and distances between vehicles to provide insights about possible high-risk areas. We achieve good prediction and recognition of car crashes without using any labeled training data of crashes. Experiments on the BrnoCompSpeed dataset show that our model can accurately monitor the road, with mean errors of 1.80% for distance measurement, 2.77 km/h for speed measurement, 0.24 m for car position prediction, and 2.53 km/h for speed prediction.