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

Multi-model particle filter-based tracking with switching dynamical state to study bedload transport 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. Keywords Visual object tracking · Multiple targets tracking · Particle filter · Switching dynamical state · Detector confidence · Bedload transport 1 Introduction mobile bed. The aim is to track all beads (see Fig. 2) over a long time to obtain trajectories, particle velocities and http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Machine Vision and Applications Springer Journals

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

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Computer Science; Pattern Recognition; Image Processing and Computer Vision; Communications Engineering, Networks
ISSN
0932-8092
eISSN
1432-1769
D.O.I.
10.1007/s00138-018-0925-z
Publisher site
See Article on Publisher Site

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. Keywords Visual object tracking · Multiple targets tracking · Particle filter · Switching dynamical state · Detector confidence · Bedload transport 1 Introduction mobile bed. The aim is to track all beads (see Fig. 2) over a long time to obtain trajectories, particle velocities and

Journal

Machine Vision and ApplicationsSpringer Journals

Published: Apr 20, 2018

References

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