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Acta Optica Sinica, Volume. 39, Issue 6, 0615004(2019)

Correlation Filter Tracking Algorithm for Adaptive Feature Selection

Wanjun Liu1, Hu Sun2、*, and Wentao Jiang1
Author Affiliations
  • 1 School of Software, Liaoning Technical University, Huludao, Liaoning 125105, China
  • 2 Graduate School, Liaoning Technical University, Huludao, Liaoning 125105, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (13)
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    References (46)
    Cited By (4)
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    Figures & Tables(13)
    Schematic of overall frame

    Fig. 1. Schematic of overall frame

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    Schematic of adaptive selection detection tracking

    Fig. 2. Schematic of adaptive selection detection tracking

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    Schematic of redetection process

    Fig. 3. Schematic of redetection process

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    Trajectory of object motion

    Fig. 4. Trajectory of object motion

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    Schematic of predicted area

    Fig. 5. Schematic of predicted area

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    Precisions and success rates for 8 tracking algorithms on OTB50. (a) Precision comparison; (b) success rate comparison

    Fig. 6. Precisions and success rates for 8 tracking algorithms on OTB50. (a) Precision comparison; (b) success rate comparison

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    Precisions and success rates of 8 tracking algorithms on OTB100. (a) Precision comparison; (b) success rate comparison

    Fig. 7. Precisions and success rates of 8 tracking algorithms on OTB100. (a) Precision comparison; (b) success rate comparison

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    Tracking results of 8 tracking algorithms in partial sequences

    Fig. 8. Tracking results of 8 tracking algorithms in partial sequences

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    • Table 1. Multi-weight distribution of fusion features

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      Table 1. Multi-weight distribution of fusion features

      NumberFilter weightColor weightTotal weight
      10.70.31
      20.30.71
      30.50.51

    • Table 2. Comparison of central positions of targets

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      Table 2. Comparison of central positions of targets

      FrameDetection positionFeature pointRedetection positionActual position
      1(115.5,188.0)--(115.5,188.0)
      2(115.0,195.2)--(116.5,193.5)
      3(112.0,202.5)--(110.5,201.0)
      4(107.9,203.7)--(107.5,202.5)
      5(107.8,219.1)178(108.3,205.2)(109.5,207.0)
      6(102.5,212.4)198(101.3,215.7)(99.5,217.5)
      7(93.5,212.7)226(91.1,213.8)(89.5,215.5)
      8(79.1,202.8)120(68.2,194.0)(66.0,194.5)
      9(66.0,188.8)--(67.5,188.5)
      10(79.1,182.3)--(76.5,184.5)

    • Table 3. Estimated scale and actual scale information

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      Table 3. Estimated scale and actual scale information

      Estimated scaleFrame
      22324252627282
      1(44,27)(45,28)(46,29)(47,29)(48,30)(51,32)(53,33)
      2(45,28)(46,29)(47,29)(48,30)(49,30)(52,32)(54,34)
      3(45,28)(47,29)(48,30)(49,30)(50,31)(53,33)(55,34)
      4(46,29)(48,30)(49,30)(50,31)(51,32)(54,34)(57,35)
      5(47,29)(49,30)(50,31)(51,32)(52,32)(55,34)(58,36)
      6(48,30)(50,31)(51,32)(52,32)(53,33)(57,35)(59,36)
      7(49,30)(51,32)(52,32)(53,33)(54,34)(58,36)(60,37)
      8(50,31)(52,32)(53,33)(54,34)(55,34)(59,36)(61,38)
      9(51,32)(53,33)(54,34)(55,34)(57,35)(60,37)(62,39)
      10(52,32)(54,34)(55,34)(57,35)(58,36)(61,38)(64,39)
      11(53,33)(55,34)(57,35)(58,36)(59,36)(62,39)(65,40)
      12(54,34)(57,35)(58,36)(59,36)(60,37)(64,39)(66,41)
      13(55,34)(58,36)(59,36)(60,37)(61,38)(65,40)(68,42)
      14(57,35)(59,36)(60,37)(61,38)(62,39)(66,41)(69,43)
      15(58,36)(60,37)(61,38)(62,39)(64,39)(68,42)(70,44)
      16(59,36)(61,38)(62,39)(64,39)(65,40)(69,43)(72,44)
      17(60,37)(62,39)(64,39)(65,40)(66,41)(70,44)(73,45)
      18(32,20)(33,21)(34,21)(34,21)(35,22)(37,23)(39,24)
      19(32,20)(34,21)(34,21)(35,22)(36,22)(38,24)(40,25)
      20(33,21)(34,21)(35,22)(36,22)(37,23)(39,24)(40,25)
      21(34,21)(35,22)(36,22)(37,23)(37,23)(40,25)(41,25)
      22(34,21)(36,22)(37,23)(37,23)(38,24)(40,25)(42,26)
      23(35,22)(37,23)(37,23)(38,24)(39,24)(41,25)(43,27)
      24(36,22)(37,23)(38,24)(39,24)(40,25)(42,26)(44,27)
      25(37,23)(38,24)(39,24)(40,25)(40,25)(43,27)(45,28)
      26(37,23)(39,24)(40,25)(40,25)(41,25)(44,27)(45,28)
      27(38,24)(40,25)(40,25)(41,25)(42,26)(45,28)(46,29)
      28(39,24)(40,25)(41,25)(42,26)(43,27)(45,28)(47,29)
      29(40,25)(41,25)(42,26)(43,27)(44,27)(46,29)(48,30)
      30(40,25)(42,26)(43,27)(44,27)(45,28)(47,29)(49,30)
      31(41,25)(43,27)(44,27)(45,28)(45,28)(48,30)(50,31)
      32(42,26)(44,27)(45,28)(45,28)(46,29)(49,30)(51,32)
      33(43,27)(45,28)(45,28)(46,29)(47,29)(50,31)(52,32)
      Optimum scale(41,25)(45,28)(46,29)(47,29)(49,30)(52,32)(53,33)
      Actual scale(43,24)(48,25)(46,27)(47,27)(53,28)(55,26)(54,27)

    • Table 4. Average tracking performance comparison among eight tracking algorithms

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      Table 4. Average tracking performance comparison among eight tracking algorithms

      AlgorithmfDSSTSAMFSRDCFBACFSTAPLESTRCFECOOurs
      Mean DPOTB500.6960.6530.7360.7350.6930.8310.8680.789
      OTB1000.7420.7410.7780.8010.7950.8550.8860.819
      Mean OPOTB500.6230.5580.6520.6730.6050.6840.6980.675
      OTB1000.5830.5540.6120.6430.6040.6800.6970.654

    • Table 5. Average tracking speed comparison among 8 tracking algorithms

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      Table 5. Average tracking speed comparison among 8 tracking algorithms

      AlgorithmfDSSTSAMFSRDCFBACFSTRCFSTAPLEECOProposed
      Mean FPS /(frame·s-1)OTB5074.7819.816.4627.9218.7655.511.4371.43
      OTB10088.4422.806.4631.9221.9068.831.3872.61
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    Wanjun Liu, Hu Sun, Wentao Jiang. Correlation Filter Tracking Algorithm for Adaptive Feature Selection[J]. Acta Optica Sinica, 2019, 39(6): 0615004

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    Paper Information

    Category: Machine Vision

    Received: Jan. 9, 2019

    Accepted: Feb. 19, 2019

    Published Online: Jun. 17, 2019

    The Author Email: Sun Hu (shzxzx@163.com)

    DOI:10.3788/AOS201939.0615004

    Topics

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