How Can I See My Future? FvTraj: Using First-person View for Pedestrian Trajectory Prediction

Huikun Bi, Ruisi Zhang, Tianlu Mao, Zhigang Deng, Zhaoqi Wang ;


This work presents a novel First-person View based Trajectory predicting model (FvTraj) to estimate the future trajectories of pedestrians in a scene given their observed trajectories and the corresponding first-person view images. First, we render first-person view images using our in-house built First-person View Simulator (FvSim), given the ground-level 2D trajectories. Then, based on multi-head attention mechanisms, we design a social-aware attention module to model social interactions between pedestrians, and a view-aware attention module to capture the relations between historical motion states and visual features from the first-person view images. Our results show the dynamic scene contexts with ego-motions captured by first-person view images via FvSim are valuable and effective for trajectory prediction. Using this simulated first-person view images, our well structured FvTraj model achieves state-of-the-art performance."

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