Predicting the Driver's Focus of Attention: The DR(eye)VE Project
By Andrea Palazzi, Davide Abati, Simone Calderara, Francesco Solera, Rita Cucchiara. 2017
322 citations recorded by the source corpus.
In this work we aim to predict the driver's focus of attention. The goal is to estimate what a person would pay attention to while driving, and which part of the scene around the vehicle is more critical for the task. To this end we propose a new computer vision model based on a multi-branch deep architecture that integrates three sources of information: raw video, motion and scene semantics. We also introduce DR(eye)VE, the largest dataset of driving scenes for which eye-tracking annotations are available. This dataset features more than 500,000 registered frames, matching ego-centric views (from glasses worn by drivers) and car-centric views (from roof-mounted camera), further enriched by other sensors measurements. Results highlight that several attention patterns are shared across drivers and can be reproduced to some extent. The indication of which elements in the scene are likely to capture the driver's attention may benefit several applications in the context of human-vehicle interaction and driver attention analysis.
Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Human-Computer Interaction
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Ruohan Zhang
- Dana H. Ballard
- Scott Niekum
- Akanksha Saran
- Mary M. Hayhoe
- Simone Calderara
- Davide Abati
- Lin Guan
In Computer Vision and Pattern Recognition
- Ruohan Zhang
- Scott Niekum
- Akanksha Saran
- Simone Calderara
- Angelo Porrello
- Rita Cucchiara
- Dana H. Ballard
- Mary M. Hayhoe
In Human-Computer Interaction
- Ruohan Zhang
- Scott Niekum
- Akanksha Saran
- Dana H. Ballard
- Lin Guan
- Mary M. Hayhoe
- Sihang Guo
- Zhuode Liu
Citations in the corpus
- Latent Space Autoregression for Novelty Detection (2018, methodology): “To this end, we employ the DR(eye)VE dataset [30], introduced for the prediction of focus of attention in driving contexts.”
- Human Gaze Assisted Artificial Intelligence: A Review (2020, methodology): “Convolutional neural networks remain the most popular tool for predicting human attention [Li et al. , 2018; Zhang et al. , 2018a; Palazzi et al. , 2018; Deng et al. , 2019].”
- Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset (2019, methodology): “Several previous works have shown these signals are helpful for gaze prediction in visuomotor tasks (Zhang et al. 2018b; Palazzi et al. 2018; Li, Liu, and Rehg 2018).”