Online multi-model particle filter-based tracking to study bedload transport - Université Jean-Monnet-Saint-Étienne Access content directly
Conference Poster Year : 2016

Online multi-model particle filter-based tracking to study bedload transport

Abstract

Multi-object tracking is a difficult problem underlying many computer vision applications. In this work, we focus on sediment transport experiments in a flow were sediments are represented by spherical calibrated 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 an adapted multiple motion model. Additionally, this algorithm integrates several improvements to account for the lack of precision of the detector. The evaluation was made using a test sequence with a dedicated ground-truth. The results show that the method outperforms state-of-the-art concurrent algorithms.
Fichier principal
Vignette du fichier
poster_icip_2016_lafaye.pdf (1.27 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

ujm-01491863 , version 1 (17-03-2017)

Identifiers

  • HAL Id : ujm-01491863 , version 1

Cite

Hugo Lafaye de Micheaux, Christophe Ducottet, Philippe Frey. Online multi-model particle filter-based tracking to study bedload transport. IEEE International Conference on Image Processing (ICIP 2016), Sep 2016, Phoenix, AZ, United States. 2016. ⟨ujm-01491863⟩
67 View
82 Download

Share

Gmail Facebook Twitter LinkedIn More