Traffic accident prediction using vehicle tracking and trajectory analysis
Intelligent visual surveillance for road vehicles is a key component for developing autonomous intelligent transportation systems. In this paper, a probabilistic model for prediction of traffic accidents using 3D model based vehicle tracking is proposed. Sample data including motion trajectories are first obtained by 3D model based vehicle tracking. A fuzzy self-organizing neural network algorithm is then applied to learn activity patterns from the sample trajectories. Vehicle activities are finally predicted by locating and matching each observed partial trajectory with the learned activity patterns, and the occurrence probability of a traffic accident is determined. Experiments with a model scene show the effectiveness of the proposed algorithm.