3D Location and Trajectory Reconstruction of a Moving Object Behind Scattering Media

被引:7
|
作者
Deng, Rujia [1 ,2 ]
Jin, Xin [1 ]
Du, Dongyu [1 ,2 ]
机构
[1] Tsinghua Univ, Shenzhen Int Grad Sch, Shenzhen Key Lab Broadband Network & Multimedia, Shenzhen 518055, Peoples R China
[2] Tsinghua Innovat Ctr Zhuhai, Zhuhai 519080, Peoples R China
基金
中国国家自然科学基金;
关键词
Scattering imaging; inverse problems; locating moving objects; TRACKING; TARGETS;
D O I
10.1109/TCI.2022.3170651
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Reconstructing an object's location and monitoring its movement through scattering media remains a significant challenge in applications. Existing methods suffer from the object motion limit, the prior of the object information, or the complex optical setup. Here, we focus on reconstructing the 3D location and trajectory of an object in motion behind scattering media by explicitly modeling and inverting the time-varying light transportation. A time-varying scattering imaging model is derived to encode the positions of the moving object in the intensity videos captured by a conventional RGB camera. Based on the model, we find that the object lies on 3D surfaces determined by point pairs on the scattering media. We then develop a back-projection method to build a 3D confidence map for the voxelized object space to find the voxel with the maximum confidence as the object position in the reconstructed trajectory at the corresponding video time. The effectiveness of the proposed method to locate moving self-illuminated and light-reflective objects in different shapes behind scattering media with different thicknesses using 2D intensity images is verified by simulated experiments and real scattering imaging systems. The reconstructions of multiple objects and different lighting conditions are discussed.
引用
收藏
页码:371 / 384
页数:14
相关论文
共 50 条
  • [31] The perceptive workbench: Computer-vision-based gesture tracking, object tracking, and 3D reconstruction for augmented desks
    Starner, T
    Leibe, B
    Minnen, D
    Westyn, T
    Hurst, A
    Weeks, J
    MACHINE VISION AND APPLICATIONS, 2003, 14 (01) : 59 - 71
  • [32] Video Based Reconstruction of 3D People Models
    Alldieck, Thiemo
    Magnor, Marcus
    Xu, Weipeng
    Theobalt, Christian
    Pons-Moll, Gerard
    2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2018, : 8387 - 8397
  • [33] Effective 3D object detection based on detector and tracker
    Nie, Weizhi
    Liu, Anan
    Wang, Zhongyang
    Su, Yuting
    NEUROCOMPUTING, 2016, 215 : 63 - 70
  • [34] Survey and systematization of 3D object detection models and methods
    Drobnitzky, Moritz
    Friederich, Jonas
    Egger, Bernhard
    Zschech, Patrick
    VISUAL COMPUTER, 2024, 40 (03) : 1867 - 1913
  • [35] Survey and systematization of 3D object detection models and methods
    Moritz Drobnitzky
    Jonas Friederich
    Bernhard Egger
    Patrick Zschech
    The Visual Computer, 2024, 40 : 1867 - 1913
  • [36] Monocular Object Detection Using 3D Geometric Primitives
    Carr, Peter
    Sheikh, Yaser
    Matthews, Iain
    COMPUTER VISION - ECCV 2012, PT I, 2012, 7572 : 864 - 878
  • [37] Towards Scene Understanding with Detailed 3D Object Representations
    Zia, M. Zeeshan
    Stark, Michael
    Schindler, Konrad
    INTERNATIONAL JOURNAL OF COMPUTER VISION, 2015, 112 (02) : 188 - 203
  • [38] Object Segmentation and Ground Truth in 3D Embryonic Imaging
    Rajasekaran, Bhavna
    Uriu, Koichiro
    Valentin, Guillaume
    Tinevez, Jean-Yves
    Oates, Andrew C.
    PLOS ONE, 2016, 11 (06):
  • [39] Vortex technique to track 3D object displacement in CGH
    Villamizar Amado, Astrid Lorena
    Velez-Zea, Alejandro
    Tebaldi, Myrian
    JOURNAL OF OPTICS, 2022, 24 (07)
  • [40] HOnnotate: A method for 3D Annotation of Hand and Object Poses
    Hampali, Shreyas
    Rad, Mahdi
    Oberweger, Markus
    Lepetit, Vincent
    2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2020, : 3193 - 3203