Continuous Head Movement Estimator for Driver Assistance: Issues, Algorithms, and On-Road Evaluations

Analysis of a driver's head behavior is an integral part of a driver monitoring system. In particular, the head pose and dynamics are strong indicators of a driver's focus of attention. Many existing state-of-the-art head dynamic analyzers are, however, limited to single-camera perspectives, which are susceptible to occlusion of facial features from spatially large head movements away from the frontal pose. Nonfrontal glances away from the road ahead, however, are of special interest since interesting events, which are critical to driver safety, occur during those times. In this paper, the authors present a distributed camera framework for head movement analysis, with emphasis on the ability to robustly and continuously operate even during large head movements. The proposed system tracks facial features and analyzes their geometric configuration to estimate the head pose using a 3-D model. Two such solutions are presented that additionally exploit the constraints that are present in a driving context and video data to improve tracking accuracy and computation time. Furthermore, the authors conduct a thorough comparative study with different camera configurations. For experimental evaluations, a novel head pose data set was collected from naturalistic on-road driving in urban streets and freeways, with particular emphasis on events inducing spatially large head movements (e.g., merge and lane change). The analyses show promising results.

Language

  • English

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Filing Info

  • Accession Number: 01527809
  • Record Type: Publication
  • Files: TLIB, TRIS
  • Created Date: May 5 2014 11:56AM