Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction
Abstract
In this paper, we present an end-to-end future-prediction model that focuses on pedestrian safety. Specifically, our model uses previous video frames, recorded from the perspective of the vehicle, to predict if a pedestrian will cross in front of the vehicle. The long term goal of this work is to design a fully autonomous system that acts and reacts as a defensive human driver would --- predicting future events and reacting to mitigate risk. We focus on pedestrian-vehicle interactions because of the high risk of harm to the pedestrian if their actions are miss-predicted. Our end-to-end model consists of two stages: the first stage is an encoder/decoder network that learns to predict future video frames. The second stage is a deep spatio-temporal network that utilizes the predicted frames of the first stage to predict the pedestrian's future action. Our system achieves state-of-the-art accuracy on pedestrian behavior prediction and future frames prediction on the Joint Attention for Autonomous Driving (JAAD) dataset.
Cite
@article{arxiv.1910.09077,
title = {Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction},
author = {Mohamed Chaabane and Ameni Trabelsi and Nathaniel Blanchard and Ross Beveridge},
journal= {arXiv preprint arXiv:1910.09077},
year = {2020}
}