A method for the treatment of pedestrian trajectory data noise

George Kouskoulis, Constantinos Antoniou, Ioanna Spyropoulou

Research output: Contribution to journalConference articlepeer-review

7 Scopus citations

Abstract

This paper provides an improved algorithm for eliminating noise of pedestrian trajectory data. Data have been collected from the field through video recordings. A semi-automatic process extracts pedestrian trajectories that include noise. The proposed algorithm relies on the Kalman filter framework. In particular, the Unscented Kalman Filter is employed for relaxing standard Kalman filter assumptions. An innovation of this paper is the incorporation of moving average in the Unscented Kalman Filter that provides more accurate pedestrian trajectory estimations. In addition, a procedure for evaluating Kalman filter noise covariance matrices is suggested. Algorithm results from real pedestrian trajectory data indicate high efficacy level in reducing data noise, thus improving their usefulness for calibrating and validating pedestrian simulation models.

Original languageEnglish
Pages (from-to)782-798
Number of pages17
JournalTransportation Research Procedia
Volume41
DOIs
StatePublished - 2019
EventInternational Scientific Conference on Mobility and Transport Urban Mobility ? Shaping the Future Together mobil.TUM 2018 - Munich, Germany
Duration: 13 Jun 201814 Jun 2018

Keywords

  • Data noise reduction
  • Unscented Kalman Filter
  • symmetric Simple Moving Average
  • trajectory data

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