Multi-model particle filter-based tracking with switching dynamical state to study bedload transport - Université Jean-Monnet-Saint-Étienne Access content directly
Journal Articles Machine Vision and Applications Year : 2018

Multi-model particle filter-based tracking with switching dynamical state to study bedload transport

Abstract

Multi-object tracking is a difficult problem underlying many computer vision applications. In this work, we focus on bedload sediment transport experiments in a turbulent flow were sediments are represented by small spherical calibrated glass beads. The aim is to track all beads over long time sequences to obtain sediment velocities and concentration. Classical algorithms used in fluid mechanics fail to track the beads over long sequences with a high precision because they incorrectly handle both miss-detections and detector imprecision. Our contribution is to propose a particle filter-based algorithm including a multiple motion model adapted to our problem. Additionally, this algorithm includes several improvements such as the estimation of the detector confidence to account for the lack of precision of the detector. The evaluation was made using two test sequences-one from our experimental setup and one from a simulation created numerically-with their dedicated ground truths. The results show that this algorithm outperforms state-of-the-art concurrent algorithms.
Fichier principal
Vignette du fichier
paper-mva.pdf (1.46 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

ujm-01782169 , version 1 (01-05-2018)

Identifiers

Cite

Hugo Lafaye de Micheaux, Christophe Ducottet, Philippe Frey. Multi-model particle filter-based tracking with switching dynamical state to study bedload transport. Machine Vision and Applications, 2018, 29 (5), pp.735-747. ⟨10.1007/s00138-018-0925-z⟩. ⟨ujm-01782169⟩
65 View
432 Download

Altmetric

Share

Gmail Facebook X LinkedIn More