Enhanced pedestrian trajectory prediction via overlapping field-of-view domains and integrated Kolmogorov-Arnold networks

Abstract

Accurate pedestrian trajectory prediction is crucial for applications such as autonomous driving and crowd surveillance. This paper proposes the OV-SKTGCNN model, an enhancement to the Social-STGCNN model, aimed at addressing its low prediction accuracy and limitations in dealing with forces between pedestrians. By rigorously dividing monocular and binocular overlapping visual regions and utilizing different influence factors, the model pedestrian interactions more realistically. The Kolmogorov-Arnold Networks (KANs) combined with Temporal Convolutional Networks (TCNs) greatly improve the ability to extract temporal features. Experimental results on the ETH and UCY datasets demonstrate that the model reduces the Final Displacement Error (FDE) by an average of 23% and the Average Displacement Error (ADE) by 18% compared to Social-STGCNN. The proposed OV-SKTGCNN model demonstrates improved prediction accuracy and better captures the subtleties of pedestrian movements.