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Acta Optica Sinica, Volume. 39, Issue 2, 0215001(2019)

Tracking Algorithm Based on Correlation Filter Fusing with Keypoint Matching

Zhe Zhang*, Jin Sun, and Liutao Yang
Author Affiliations
  • College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, China
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    AbstractGet PDF(in Chinese)
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    Figures & Tables(7)
    Framework of long-term tracking with CFMK

    Fig. 1. Framework of long-term tracking with CFMK

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    Response fusion of multiple features

    Fig. 2. Response fusion of multiple features

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    Tracking results for nine kinds of trackers on different databases. (a) Tracking success rate and (b) tracking precisionfor OTB-13 database; (c) tracking success rate and (d) tracking precision for OTB-15 database

    Fig. 3. Tracking results for nine kinds of trackers on different databases. (a) Tracking success rate and (b) tracking precisionfor OTB-13 database; (c) tracking success rate and (d) tracking precision for OTB-15 database

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    Comparison of tracking success rate for each algorithm under eleven kinds of scenes. (a) In-plane rotation; (b) low resolution; (c) object occlusion; (d) out of view; (e) out-of-plane rotation; (f) scale variation; (g) fast motion; (h) background clutter; (i) motion blur; (j) object deformation; (k) illumination variation

    Fig. 4. Comparison of tracking success rate for each algorithm under eleven kinds of scenes. (a) In-plane rotation; (b) low resolution; (c) object occlusion; (d) out of view; (e) out-of-plane rotation; (f) scale variation; (g) fast motion; (h) background clutter; (i) motion blur; (j) object deformation; (k) illumination variation

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    Tracking result graphs in partial sequences for six kinds of tracking algorithms. (a) BlurOwl; (b) Liquor; (c) Box; (d) Jogging 1; (e) Jogging 2; (f) Tigger

    Fig. 5. Tracking result graphs in partial sequences for six kinds of tracking algorithms. (a) BlurOwl; (b) Liquor; (c) Box; (d) Jogging 1; (e) Jogging 2; (f) Tigger

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    • Table 1. Mean DP and OP on different databases for proposed algorithm under different values of K

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      Table 1. Mean DP and OP on different databases for proposed algorithm under different values of K

      Value of KMean DPMean OP
      OTB-13OTB-15OTB-13OTB-15
      10.8850.7960.6420.569
      20.8710.7880.6300.562
      30.8740.7960.6330.569
      40.8710.7950.6320.568
      50.8710.7950.6330.567

    • Table 2. Mean tracking performance parameters on different databases for nine kinds of tracking algorithms

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      Table 2. Mean tracking performance parameters on different databases for nine kinds of tracking algorithms

      AlgorithmMean FPS (OTB-15) /(frame·s-1)Mean DPMean OP
      OTB-13OTB-15OTB-13OTB-15
      CSK440.410.5450.5180.3980.382
      KCF98.490.7040.6750.5000.470
      DSST46.440.7370.6930.5540.520
      LCT24.180.8480.7620.5930.527
      SKSCF39.290.8640.7720.6230.549
      STAPLE76.700.7820.7840.5930.578
      SRDCF5.420.8380.7890.6260.598
      BACF32.120.8490.8170.6450.616
      Proposed29.830.8850.7960.6420.569
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    Zhe Zhang, Jin Sun, Liutao Yang. Tracking Algorithm Based on Correlation Filter Fusing with Keypoint Matching[J]. Acta Optica Sinica, 2019, 39(2): 0215001

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    Paper Information

    Category: Machine Vision

    Received: Jul. 24, 2018

    Accepted: Sep. 17, 2018

    Published Online: May. 10, 2019

    The Author Email:

    DOI:10.3788/AOS201939.0215001

    Topics

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