Channel spatio-temporal convolutional network for pedestrian trajectory prediction

Pedestrian trajectory prediction is a crucial technology for agents to assist human beings, which remains highly challenging due to the complex interactions between pedestrians and the environment. However, previous works based on pedestrian relative position modeling have the problem of ignoring environmental information and global pedestrian perception, which inevitably leads to a significant deviation from reality. To address these challenges, we introduce a Channel Spatio-temporal Convolutional Network (CSTCN) for predicting pedestrian trajectories. The CSTCN explicitly models pedestrian interactions with perceptual information to capture the temporal and spatial characteristics of pedestrians. Meanwhile, we use Group-SE to model the sensitivity of pedestrians to multi-channel data, which facilitates predictions based on historically observed trajectories. We evaluated our proposed method on the ETH and UCY datasets. The experimental results demonstrate that our method outperforms other state-of-the-art methods by 11.4% in Average Displacement Error (ADE) and 6.7% in Final Displacement Error (FDE).