title: Bayesian estimation-based pedestrian tracking in microcells.
abstract: We consider a pedestrian tracking system where sensor nodes are placed only at specific points so that the monitoring region is divided into multiple smaller regions referred to as microcells. In the proposed pedestrian tracking system, sensor nodes composed of pairs of binary sensors can detect pedestrian arrival and departure events. In this paper, we focus on pedestrian tracking in microcells. First, we investigate actual pedestrian trajectories in a microcell on the basis of observations using video sequences, after which we prepare a pedestrian mobility model. Next, we propose a method for pedestrian tracking in microcells based on the developed pedestrian mobility model. In the proposed method, we extend the Bayesian estimation to account for time-series information to estimate the correspondence between pedestrian arrival and departure events. Through simulations, we show that the tracking success ratio of the proposed method is increased by 35.8% compared to a combinatorial optimization-based tracking method.
authors.affiliation: The Cybermedia Center, Osaka University, Toyonaka 560-0043, Japan.
authors.forename: Yoshiaki
authors.initials: Y
authors.lastname: Taniguchi
authors.forename: Masahiro
authors.initials: M
authors.lastname: Sasabe
authors.forename: Satoshi
authors.initials: S
authors.lastname: Aihara
authors.forename: Hirotaka
authors.initials: H
authors.lastname: Nakano
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D006801:Humans; D015203:Reproducibility of Results; D013997:Time Factors; D016138:Walking; D059015:Wireless Technology