DGAZE is a new dataset for mapping the driver's gaze onto the road. Currently, driver gaze datasets are collected using eye-tracking hardware which are expensive and cumbersome, and thus unsuited for use during testing. Thus, our dataset is designed so that no costly equipment is required during test time. Models trained using our dataset requires only a dashboard-mounted mobile phone during deployment, as our data is collected using mobile phones. We collect the data in a lab setting with a video of a road projected in front of the driver. We overcome the limitation of not using eye trackers by annotating points on the road video and asking the drivers to look at them. For more details, please refer to our paper.
[ paper ]
We collected road videos using mobile phones mounted on the dashboards of cars driven in the city. We combined the road videos to create a single 18-minute video that had a good mix of road, lighting, and traffic conditions. The road images have varied illumination as the images are captured from morning to evening in the real cars on actual roads. For each frame, we annotated a single object belonging to one of the classes: car, bus, motorbike, pedestrian, auto-rickshaw, traffic signal and sign board. We also marked the center of each bounding box to serve as the groundtruth for the point detection challenge. We annotated objects that typically take up a driver's attention such as relevant signage, pedestrians, and intercepting vehicles.

The task of driver eye-gaze can be solved as point-wise prediction or object-wise prediction. We provide annotation so that our dataset can be used for pointwise as well as object-wise prediction. Both types of eye-gaze prediction are useful. Predicting the object which the driver is looking at is useful for higher-level ADAS systems. This may be done by getting object candidates using an object detection algorithm and using the eye gaze to predict which object is being observed. Object prediction can be used to determine whether a driver is focusing on a pedestrian or if they noticed a signboard for example. Point-wise prediction is much more fine-grained and are more useful for nearby objects, as they show which part of the object is being focused on. They can be used to determine the saccade patterns of the eyes or to create a heatmap of the attention of a driver. They may even be converted into object-wise prediction. Our dataset allows both types of analyses to be conducted.

 @inproceedings{isha2020iros, 

          title={DGAZE: Driver Gaze Mapping on Road}, 

          author={Dua, Isha and John, Thrupthi Ann and Gupta, Riya and Jawahar, CV}, 

          booktitle={2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020}, 

          year={2020} 

}