Pedestrian Trajectory Prediction Method Based on Multi-Stream Feature Fusion in Egocentric Perspective

Accurately predicting the future trajectories of surrounding pedestrians is of great significance for the safety decision-making of intelligent vehicles. In this paper, a pedestrian trajectory prediction method based on multi-stream information fusion from a egocentric perspective is proposed. First, the raw images are preprocessed into pedestrian detection boxes and corresponding optical flow maps. Then, the detection box sequences, pedestrian region of interest (ROI) optical flow, and fixed center region optical flow are fed into three GRU-based feature extraction channels. A cross-attention mechanism is designed to closely integrate the bounding box and optical flow information. Finally, the motion information of the ego vehicle is compensated into the trajectory prediction to enhance prediction accuracy. Experiments on the PIE dataset demonstrate the effectiveness of the proposed method. Compared to current state-of-the-art methods, our approach shows an advantage in prediction accuracy.