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.
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https://hal-ujm.archives-ouvertes.fr/ujm-01387162
Contributor : Christophe Ducottet <>
Submitted on : Tuesday, October 25, 2016 - 11:22:35 AM
Last modification on : Monday, October 14, 2019 - 3:24:04 PM

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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. pp.3489 - 3493, ⟨10.1109/ICIP.2016.7533008⟩. ⟨ujm-01387162⟩

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