Dense Residual Networks for Gaze Mapping on Indian Roads

TL;DR

This paper introduces DR-Gaze, a dense residual network architecture tailored for gaze mapping in Indian driving scenarios, addressing a gap in existing research focused mainly on European and American roads.

Contribution

The paper presents a novel deep learning architecture specifically designed for Indian road conditions, improving gaze mapping accuracy in autonomous driving contexts.

Findings

Outperforms previous methods on the DGAZE dataset

Demonstrates effectiveness of dense residual networks for gaze mapping

Addresses unique challenges of Indian driving environments

Abstract

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.