3D Modeling of Riverbeds Based on NURBS Algorithm

Yang, Kaiyuan; Zhong, Caili; Zhang, Xiaotian; Zhu, Xijing; Yue, Yong

doi:10.1145/3430199.3430239

Cited by 1 publication

(1 citation statement)

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“…A B-spline curve can be fitted to arbitrary shapes by adjusting the control points to estimate the shape of the extended target shape [17] assumed that the measurement points are generated using an edge, which is not applicable for some targets, especially group targets. In addition, non-uniform rational B-splines (NURBS) surfaces which can be considered as an extension of B-spline have been successfully applied to estimate a 3-dimensional (3D) target extension [18,19]. However, although NURBS is more flexible, it needs extra storage to define traditional curves and surfaces.…”

Section: Introductionmentioning

confidence: 99%

Non-Ellipsoidal Infrared Group/Extended Target Tracking Based on Poisson Multi-Bernoulli Mixture Filter and B-Spline

et al. 2023

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This study provides a solution for multiple group/extended target tracking with an arbitrary shape. Many tracking approaches for extended/group targets have been proposed. However, these approaches make assumptions about the target shape, which have limitations in practical applications. To address this problem, in this work, an extended/group target tracking algorithm based on B-spline is proposed. Specifically, the extension of an extended or a group target was modeled as a spatial probability distribution characterized by the control points of a B-spline function that was then jointly propagated with the measurement rate model and kinematic component model over time using the Poisson multi-Bernoulli mixture (PMBM) filter framework. In addition, an amplitude-aided measurement partitioning approach is proposed to improve the accuracy caused by distance-based approaches. The simulation results demonstrate that the extension, shape and orientation of targets can be estimated better by the proposed algorithm, even if the shape changes. The tracking performance is also improved by about 10% and 13% compared to the other two algorithms.

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Section: Introductionmentioning

confidence: 99%