Monitoring head dynamics for driver assistance systems: A multi-perspective approach

Abstract

A visually demanding driving environment, where elements surrounding a driver are constantly and rapidly changing, requires a driver to make spatially large head turns. Many existing state of the art vision based head pose algorithms, however, still have difficulties in continuously monitoring the head dynamics of a driver. This occurs because, from the perspective of a single camera, spatially large head turns induce self-occlusions of facial features, which are key elements in determining head pose. In this paper, we introduce a shape feature based multi-perspective framework for continuously monitoring the driver's head dynamics. The proposed approach utilizes a distributed camera setup to observe the driver over a wide range of head movements. Using head dynamics and a confidence measure based on symmetry of facial features, a particular perspective is chosen to provide the final head pose estimate. Our analysis on real world driving data shows promising results.