Combining Deep and Depth: Deep Learning and Face Depth Maps for Driver Attention Monitoring
This work addresses driver safety by providing an incremental improvement in attention monitoring using depth data.
The paper tackled driver attention monitoring by combining deep learning with depth maps, achieving state-of-the-art results on new public datasets with real-time performance.
Recently, deep learning approaches have achieved promising results in various fields of computer vision. In this paper, we investigate the combination of deep learning based methods and depth maps as input images to tackle the problem of driver attention monitoring. Moreover, we assume the concept of attention as Head Pose Estimation and Facial Landmark Detection tasks. Differently from other proposals in the literature, the proposed systems are able to work directly and based only on raw depth data. All presented methods are trained and tested on two new public datasets, namely Pandora and MotorMark, achieving state-of-art results and running with real time performance.