Journals Articles Conferences News About CLP
Submit a paper Email Alert
Search
Search
Articles
Journals
News
Advanced Search
Top Searches
2D MaterialsTransformation opticsQuantum PhotonicsSmall lasersSemiconductor UV PhotonicsSupersymmetric microring laser arrays

Acta Optica Sinica, Volume. 38, Issue 11, 1115002(2018)

Long-Term Object Tracking Based On Feature Fusion

Baoyi Ge*, Xianzhang Zuo**, and Yongjiang Hu
Author Affiliations
  • Department of UAV Engineering, Army Engineering University, Shijiazhuang, Hebei 050003, China
    • show less
    AbstractGet PDF(in Chinese)
    Figures&Tables (12)
    Equations (0)
    References (0)
    Cited By (7)
    Tools
    Paper Information
    Topics
    Figures & Tables(12)
    Framework of proposed tracking algorithm

    Fig. 1. Framework of proposed tracking algorithm

    Download full size
    Object feature visualization. (a) Image; (b) HOG; (c) LSH; (d)-(f) CN

    Fig. 2. Object feature visualization. (a) Image; (b) HOG; (c) LSH; (d)-(f) CN

    Download full size
    Region proposal

    Fig. 3. Region proposal

    Download full size
    Flow chart of proposed algorithm

    Fig. 4. Flow chart of proposed algorithm

    Download full size
    Tracking results of different target detection threshold

    Fig. 5. Tracking results of different target detection threshold

    Download full size
    Tracking results evaluation plots on OTB100

    Fig. 6. Tracking results evaluation plots on OTB100

    Download full size
    Partial object tracking results and overlap rates on OTB100. (a) Panda; (b) Lemming; (c) Blur Owl; (d) Human5; (e) Soccer; (f) Wake board1; (g) Jogging-2

    Fig. 7. Partial object tracking results and overlap rates on OTB100. (a) Panda; (b) Lemming; (c) Blur Owl; (d) Human5; (e) Soccer; (f) Wake board1; (g) Jogging-2

    Download full size
    OPE evaluation on UAV123. (a) Precision; (b) success rate

    Fig. 8. OPE evaluation on UAV123. (a) Precision; (b) success rate

    Download full size
    Tracking results in practical applications. (a) Bicycle; (b) child; (c) girl

    Fig. 9. Tracking results in practical applications. (a) Bicycle; (b) child; (c) girl

    Download full size

    • Table 1. Success rate of object tracking

      View table

      Table 1. Success rate of object tracking

      AlgorithmSuccess rateTracking speed /(frame·s-1)
      Proposed0.77828.2
      BACF0.75821.2
      SRDCF0.7174.2
      LCT0.62119.7
      MUSTER0.6762.2
      DSST0.54818.6
      KCF0.536124.1

    • Table 2. Characteristics of image sequences in experiment

      View table

      Table 2. Characteristics of image sequences in experiment

      SequenceCharacteristicFrame number
      PandaSV,OCC,DEF,IPR,OPR,OV,LR1000
      LemmingIV,SV,OCC,FM,OPR,OV1336
      Blur OwlSV,MB,FM,IPR631
      Human5SV,OCC,DEF713
      Jogging-2OCC,DEF,OPR307
      SoccerIV,SV,OCC,MB,FM,IPR,OPR,BC392
      Wake board1SV,IPR,DEF,FM141

    • Table 3. Success rate of object tracking

      View table

      Table 3. Success rate of object tracking

      AlgorithmSuccess rateTracking speed /(frame·s-1)
      Proposed0.53729.8
      BACF0.50622.3
      SRDCF0.5115.6
      LCT0.32923.9
      MUSTER0.4360.9
      DSST0.31144.7
      KCF0.294307.6
    Tools

    Get Citation

    Copy Citation Text

    Baoyi Ge, Xianzhang Zuo, Yongjiang Hu. Long-Term Object Tracking Based On Feature Fusion[J]. Acta Optica Sinica, 2018, 38(11): 1115002

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Set citation alerts for article
    Save article for my favorites
    Paper Information

    Category: Machine Vision

    Received: May. 4, 2018

    Accepted: Jun. 13, 2018

    Published Online: May. 9, 2019

    The Author Email:

    DOI:10.3788/AOS201838.1115002

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology