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.
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