• 中国科技核心期刊
  • Scopus收录期刊
  • DOAJ收录期刊
  • JST收录期刊
  • Euro Pub收录期刊
Volume 28 Issue 6
 
Turn off MathJax
Article Contents
Article Navigation >  Journal of Unmanned Undersea Systems  > 2020  >  28(6): 618-625
WU Xiang, ZHONG Yu-xuang, YUE Qi-qi, LI Xiao-mao. Scale Adaptive Sea Surface Target Tracking Algorithm Based on Deep Learning[J]. Journal of Unmanned Undersea Systems, 2020, 28(6): 618-625. doi: 10.11993/j.issn.2096-3920.2020.06.005
Citation: WU Xiang, ZHONG Yu-xuang, YUE Qi-qi, LI Xiao-mao. Scale Adaptive Sea Surface Target Tracking Algorithm Based on Deep Learning[J]. Journal of Unmanned Undersea Systems, 2020, 28(6): 618-625. doi: 10.11993/j.issn.2096-3920.2020.06.005
shu

Scale Adaptive Sea Surface Target Tracking Algorithm Based on Deep Learning

doi: 10.11993/j.issn.2096-3920.2020.06.005
  • 1.

    School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China

  • 2.

    School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

  • Received Date: 2020-10-15
  • Rev Recd Date: 2020-12-02
  • Publish Date: 2020-12-31
    Abstract
  • Compared with target tracking in common scenes, sea surface target tracking presents unique challenges such as changes in the target scale and perspective as well as intense dithering of targets. Accordingly, a scale-adaptive sea surface target tracking algorithm based on deep learning is proposed. The algorithm classifies samples according to whether the central point of the sample falls to the ground truth and then regresses the distances from the target location to the four sides of the bounding box to predict the position and scale of the target. An evaluation platform for the sea surface target tracking algorithm is also established to verify the effectiveness of the proposed algorithm. Experimental results show that compared with the anchor-based algorithm, the tracking accuracy of the proposed algorithm is improved by 4.8% and its success rate is improved by 11.49%, thus effectively solving the problem of target scale adaptation.

     

    • FullText(HTML)
  • loading
    • References(15)
  • [1]
    彭艳, 陈加宏, 李小毛, 等. 时空上下文融合的无人艇 海面目标跟踪[J]. 中国科学: 技术科学, 2018, 48(12): 1357-1372.

    Peng Yan, Chen Jia-hong, Li Xiao-mao, et al. Sea Surface Object Tracking for USV with Spatio-Temporal Context Fusion[J]. Scientia Sinica Technologica, 2018, 48(12): 1357-1372.
    [2]
    Wu Y, Lim J, Yang M H. Online Object Tracking: a Benchmark[C]//IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR, USA: IEEE, 2013: 2411-2418.
    [3]
    Wu Y, Lim J, Yang M H. Object Tracking Benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834-1848.
    [4]
    Henriques J F, Caseiro R, Martins P, et al. High-Speed Tracking with Kernelized Correlation Filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 583-596.
    [5]
    Danelljan M, Hager G, Khan F S, et al. Discriminative Scale Space Tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(8): 1561-1575.
    [6]
    Li Y, Zhu J. A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration[C]//European Conference on Computer Vision. Zurich, Switzerland: ECCV, 2014: 254-265.
    [7]
    Huang D, Luo L, Wen M, et al. Enable Scale and Aspect Ratio Adaptability in Visual Tracking with Detection Proposals[C]//Proceedings of the British Machine Vision Conference. Swansea, UK: BMVC, 2015.
    [8]
    Montero A S, Lang J, Laganiere R. Scalable Kernel Correlation Filter with Sparse Feature Integration[C]//2015 IEEE International Conference on Computer Vision Workshop (ICCVW). Santiago, Chile: IEEE, 2015: 587- 594.
    [9]
    Li F, Yao Y, Li P, et al. Integrating Boundary and Center Correlation Filters for Visual Tracking with Aspect Ratio Variation[C]//IEEE International Conference on Computer Vision. Shenzhen, China: IEEE, 2017: 2001-2009.
    [10]
    Bertinetto L, Valmadre J, Henriques J F, et al. Fully-convolutional Siamese Networks for Object Tracking[C]// European Conference on Computer Vision. Amsterdam, Netherlands: ECCV, 2016: 850-865.
    [11]
    Li B, Yan J, Wu W, et al. High Performance Visual Tracking with Siamese Region Proposal Network[C]//IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, 2018: 8971-8980.
    [12]
    Ren S, He K, Girshick R, et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
    [13]
    Tian Z, Shen C, Chen H, et al. FCOS: Fully Convolutional One-Stage Object Detection[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). South Korea: IEEE, 2020.
    [14]
    Lin T Y, Goyal P, Girshick R, et al. Focal Loss for Dense Object Detection[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2020, 42(2): 318-327.
    [15]
    Zhang Z, Peng H. Deeper and Wider Siamese Networks for Real-Time Visual Tracking[EB/OL]. [2019-03-28]. https://arxiv.org/abs/1901.01660?context=cs.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article Views(887) PDF Downloads(256) Cited by()
    Proportional views
    Related
    Service
    Subscribe
    Copyright：陕ICP备09015163号Address:No. 96, Jinye Road, Xi'an, Shaanxi
    Code：710077Tel: 029-88327279QQ: 767358370
    Email: bianjibu705@sohu.com 767358370@qq.com
    Email Alert RSS
    Taobao
    Micro

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return