Paper Info
Title
MEDIRL - Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning.
Abstract
Inspired by human visual attention, we propose a novel inverse reinforcement learning formulation using Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) for predicting the visual attention of drivers in accident-prone situations. MEDIRL predicts fixation locations that lead to maximal rewards by learning a task-sensitive reward function from eye fixation patterns recorded from attentive drivers. Additionally, we introduce EyeCar, a new driver attention dataset in accident-prone situations. We conduct comprehensive experiments to evaluate our proposed model on three common benchmarks: (DR(eye)VE, BDD-A, DADA-2000), and our EyeCar dataset. Results indicate that MEDIRL outperforms existing models for predicting attention and achieves state-of-the-art performance. We present extensive ablation studies to provide more insights into different features of our proposed model.
Year
DOI
Venue
2021
10.1109/ICCV48922.2021.01293
ICCV
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Sonia Baee122.06
Erfan Pakdamanian222.40
Inki Kim300.34
Feng Lu413922.18
Vicente Ordonez5141869.65
Laura Barnes617319.98