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
Abstract
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."
BibTeX
@inproceedings{eccv2020_howcaniseemyfutu,
title = {How Can I See My Future? FvTraj: Using First-person View for Pedestrian Trajectory Prediction},
author = {Huikun Bi and Ruisi Zhang and Tianlu Mao and Zhigang Deng and Zhaoqi Wang},
booktitle = {ECCV 2020},
year = {2020}
}