Dense Residual Networks for Gaze Mapping on Indian Roads
This work addresses a domain-specific problem for autonomous driving systems in India, but it is incremental as it adapts existing methods to a new regional context.
The paper tackles the problem of mapping driver gaze onto roads in the Indian driving context, which has been less studied compared to European or American scenarios, and proposes a novel architecture called DR-Gaze, achieving results compared to previous works and state-of-the-art on the DGAZE dataset.
In the recent past, greater accessibility to powerful computational resources has enabled progress in the field of Deep Learning and Computer Vision to grow by leaps and bounds. This in consequence has lent progress to the domain of Autonomous Driving and Navigation Systems. Most of the present research work has been focused on driving scenarios in the European or American roads. Our paper draws special attention to the Indian driving context. To this effect, we propose a novel architecture, DR-Gaze, which is used to map the driver's gaze onto the road. We compare our results with previous works and state-of-the-art results on the DGAZE dataset. Our code will be made publicly available upon acceptance of our paper.