Tracking of cyclists in the velodrome using IMU and timing sensorsMS74

In this presentation we will describe novel Bayesian data fusion algorithms for localising cyclists on the velodrome race track. The path of the cyclist is described using a dynamical model expressed in the intrinsic coordinates of the bike, and we show how to fuse this with timing measurements derived from a video capture system and intertial measurements from a gyrometer and accelerometer attached to the bike. A combination of Rao-Blackwellised particle filtering/smoothing and optimal proposal particle filtering is developed for solution of this task.

This presentation is part of Minisymposium “MS74 - Sequential Monte Carlo methods for inverse estimation in imaging science
organized by: Narayan Puthanmadam Subramaniyam (Aalto University) , Sara Sommariva (Aalto University) .

Simon Godsill (University of Cambridge)
Jiaming Liang (University of Cambridge)
bayesian, bayesian methods, imu, intrinsic coordinates, inverse problems, nonlinear optimization, optimal proposal, sequential monte carlo, statistical inverse estimation methods, stochastic processes