A Probabilistic Framework for Joint Pedestrian Head and Body Orientation Estimation

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38 Citations (Scopus)

Abstract

We present a probabilistic framework for the joint estimation of pedestrian head and body orientation from a mobile stereo vision platform. For both head and body parts, we convert the responses of a set of orientation-specific detectors into a (continuous) probability density function. The parts are localized by means of a pictorial structure approach, which balances part-based detector responses with spatial constraints. Head and body orientations are estimated jointly to account for anatomical constraints. The joint single-frame orientation estimates are integrated over time by particle filtering. The experiments involved data from a vehicle-mounted stereo vision camera in a realistic traffic setting; 65 pedestrian tracks were supplied by a state-of-the-art pedestrian tracker. We show that the proposed joint probabilistic orientation estimation framework reduces the mean absolute head and body orientation error up to 15° compared with simpler methods. This results in a mean absolute head/body orientation error of about 21°/19°, which remains fairly constant up to a distance of 25 m. Our system currently runs in near real time (8-9 Hz).

Original languageEnglish
Article number7010032
Pages (from-to)1872-1882
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Volume16
Issue number4
DOIs
Publication statusPublished - 1 Aug 2015
Externally publishedYes

Keywords

  • Active pedestrian safety
  • computer vision
  • pose estimation
  • social robotics

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