A particle filter-based approach for tracking undersea narrow telecommunication cables

被引:26
|
作者
Ortiz, Alberto [1 ]
Antich, Javier [1 ]
Oliver, Gabriel [1 ]
机构
[1] Univ Balearic Isl, Dept Math & Comp Sci, Palma De Mallorca 07122, Spain
关键词
Cable tracking; Underwater vision; Autonomous underwater vehicle (AUV);
D O I
10.1007/s00138-009-0199-6
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The surveillance and inspection of underwater installations such as telecommunication cables are currently carried out by trained operators who, from the surface, guide a remotely operated vehicle (ROV) with cameras mounted over it. This manual visual control is, however, a very tedious job that tends to fail if the operator loses concentration. This paper describes a tracking system for underwater narrow telecommunication cables, the main objective of which is to allow an autonomous underwater vehicle to video-document the whole length of a cable. The approach is based on particle filters (PFs) because of their natural ability to model multi-dimensional multi-modal PDFs, which allows handling in a more appropriate way the ambiguities that naturally arise from undersea environments. In effect, despite the special visual features that artificial objects present, which allow distinguishing them in natural scenarios such as the seabed, distracting background such as rocks or algae growing on top and nearby cables, complicate the detection and tracking and give rise to ambiguities when rocks or marine growth form shapes and textures that resemble the cable. Apart from the different models that a PF requires, the paper also describes a set of added features, which successfully compensate some large errors in the cable pose estimation when the non-enhanced tracker is applied. Extensive experimental results over a test set of more than 10,000 frames, for which a ground truth has been manually generated, have shown the usefulness of the solution proposed. Besides, results for a set of six video sequences accounting for almost 150,000 frames and around one hour and a half of successful continuous video tracking are also discussed. All those images come from inspection runs captured by ROVs navigating over real telecommunication undersea cables.
引用
收藏
页码:283 / 302
页数:20
相关论文
共 50 条
  • [41] MCMC Particle Filter-based Vehicle Tracking Method Using Multiple Hypotheses and Appearance Model
    Lim, Young-Chul
    Kim, Dongyoung
    Lee, Chung-Hee
    2013 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV), 2013, : 1131 - 1136
  • [42] Scalable implementation of particle filter-based visual object tracking on network-on-chip (NoC)
    Pinalkumar Engineer
    Rajbabu Velmurugan
    Sachin Patkar
    Journal of Real-Time Image Processing, 2020, 17 : 1117 - 1134
  • [43] Particle Filter-based Multi-part Human Tracking with Failure Adjustment in Video Sequences
    Zhao, San-Lung
    Lee, Hsi-Jian
    JOURNAL OF INFORMATION SCIENCE AND ENGINEERING, 2010, 26 (06) : 2267 - 2281
  • [44] Scalable implementation of particle filter-based visual object tracking on network-on-chip (NoC)
    Engineer, Pinalkumar
    Velmurugan, Rajbabu
    Patkar, Sachin
    JOURNAL OF REAL-TIME IMAGE PROCESSING, 2020, 17 (05) : 1117 - 1134
  • [45] An Improved Correlation Filter-Based Target Tracking Method
    Liu, Jun
    Luo, Zhongqiang
    Xiong, Xingzhong
    2019 IEEE 4TH INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING (ICSIP 2019), 2019, : 219 - 223
  • [46] Filter-Based Tracking of Magnetic Levitation Ball System
    Qi, Guimei
    Wu, Zhaojing
    PROCEEDINGS OF THE 30TH CHINESE CONTROL AND DECISION CONFERENCE (2018 CCDC), 2018, : 3135 - 3139
  • [47] Kalman Filter-Based Tracking of Burst DSSS Signal
    Zhou, Xionglin
    Liu, Bo
    Cheng, Jian
    Shi, Shengchao
    2013 INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND TECHNOLOGY (ICIST), 2013, : 1145 - 1148
  • [48] Temporally-adjusted correlation filter-based tracking
    Song, Wenjie
    Li, Yang
    Zhu, Jianke
    Chen, Chun
    NEUROCOMPUTING, 2018, 286 : 121 - 129
  • [49] Particle filter-based AUV integrated navigation methods
    Zhang, Bo
    Xu, Wen
    Li, Jianlong
    Jiqiren/Robot, 2012, 34 (01): : 78 - 83
  • [50] A Particle Filter-Based Approach for Vehicle Trajectory Reconstruction Using Sparse Probe Data
    Wei, Lei
    Wang, Yunpeng
    Chen, Peng
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 22 (05) : 2878 - 2890