FORT-RAJ: A Hybrid Fisheye Model for Real-Time Pedestrian Trajectory Prediction

This paper introduces FORT-RAJ, a hybrid model designed for pedestrian trajectory prediction in the context of top-view fisheye images. To achieve this, FORT-RAJ merges the FORT (Fisheye Online Realtime Tracking) algorithm, which tracks people using fisheye cameras without prediction capabilities, with the GATraj model, known for trajectory prediction but not yet adapted for fisheye images. The proposed method, FORT-RAJ, is designed to detect pedestrians, track their trajectories, and predict their future positions. It leverages the wide field of view of fisheye cameras while addressing the distortions inherent in such images. The experiments demonstrated that the FORT-RAJ model performs satisfactorily on fisheye images, achieving an Average Displacement Error (ADE) of 0.38 meters and an Final Displacement Error (FDE) of 0.42 meters.