A contribution to 3D tracking of deformable bubbles in swarms using temporal information

被引:0
|
作者
Wang, Lantian [1 ]
Ma, Tian [1 ]
Lucas, Dirk [1 ]
Eckert, Kerstin [1 ,2 ]
Hessenkemper, Hendrik [1 ]
机构
[1] Helmholtz Zentrum Dresden Rossendorf, Inst Fluid Dynam, Bautzner Landstr 400, D-01328 Dresden, Saxony, Germany
[2] Tech Univ Dresden, Inst Proc Engn & Environm Technol, D-01069 Dresden, Saxony, Germany
关键词
IMAGE MEASUREMENT TECHNIQUE; RECONSTRUCTION; FLOWS;
D O I
10.1007/s00348-025-03963-9
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Reliable Lagrangian 3D tracking of individual bubble swarm members allows a deeper understanding of hydrodynamic bubble-bubble interactions and their collective rise. For multi-view measurements, we have recently developed such a tracking method (Hessenkemper in Int J Multiph Flow 179:104932, 2024), which is able to track deformable bubbles with low to moderate view obstruction through the bubbles to each other. In the present work, we aim to further enhance the 3D tracking performance by additionally incorporating 2D temporal information in the form of previously established 2D tracks in each camera view. The new 3D tracking method is able to disambiguate cross-view object associations at each time step by using the 2D track information accumulated over time. In addition, the temporal information from multiple 2D domains is used in two post-processing steps to improve the completeness of established 3D trajectories. Compared to the previous 3D tracking method, the extended 3D tracking framework shows noticeable improvements in tracking ability, accuracy, and completeness of trajectories. [GRAPHICS] .
引用
收藏
页数:13
相关论文
共 50 条
  • [21] 3D Lagrangian tracking of polydispersed bubbles at high image densities
    Tan, Shiyong
    Zhong, Shijie
    Ni, Rui
    EXPERIMENTS IN FLUIDS, 2023, 64 (04)
  • [22] 3D Lagrangian tracking of polydispersed bubbles at high image densities
    Shiyong Tan
    Shijie Zhong
    Rui Ni
    Experiments in Fluids, 2023, 64
  • [23] 3D Deformable Convolution Temporal Reasoning network for action recognition
    Ou, Yangjun
    Chen, Zhenzhong
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2023, 93
  • [24] Tomographic reconstruction using 3D deformable models
    Battle, XL
    Cunningham, GS
    Hanson, KM
    PHYSICS IN MEDICINE AND BIOLOGY, 1998, 43 (04): : 983 - 990
  • [25] 3D SHAPE RECOVERY USING A DEFORMABLE MODEL
    SHEN, XQ
    HOGG, D
    IMAGE AND VISION COMPUTING, 1995, 13 (05) : 377 - 383
  • [26] 3D BRAIN MAPPING USING A DEFORMABLE NEUROANATOMY
    CHRISTENSEN, GE
    RABBITT, RD
    MILLER, MI
    PHYSICS IN MEDICINE AND BIOLOGY, 1994, 39 (03): : 609 - 618
  • [28] Tracking weather storms using 3D Doppler radial velocity information
    Tang, X
    Barron, JL
    Mercer, RE
    Joe, P
    IMAGE ANALYSIS, PROCEEDINGS, 2003, 2749 : 1038 - 1043
  • [29] 3D audiovisual person tracking using Kalman: Filtering and information theory
    Katsarakis, Nikos
    Souretis, George
    Talantzis, Folios
    Pnevmatikakis, Aristodemos
    Polymenakos, Lazaros
    Multimodal Technologies for Perception of Humans, 2007, 4122 : 45 - 54
  • [30] 3D Pose Tracking Using a Recovered 3D Model
    Chen, Wei
    Zhao, Yuelong
    Chen, Shu
    Ouyang, Jianquan
    JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS, 2018, 27 (04)