Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction

Mohamed Chaabane, Ameni Trabelsi, Nathaniel Blanchard, Ross Beveridge; The IEEE Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 2297-2306

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.

Related Material


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[bibtex]
@InProceedings{Chaabane_2020_WACV,
author = {Chaabane, Mohamed and Trabelsi, Ameni and Blanchard, Nathaniel and Beveridge, Ross},
title = {Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction},
booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}