• Laser & Optoelectronics Progress
  • Vol. 56, Issue 10, 101501 (2019)
Hongji Zhu* and Fengqin Yu
Author Affiliations
  • School of Internet of Things, Jiangnan University, Wuxi, Jiangsu 214122, China
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    DOI: 10.3788/LOP56.101501 Cite this Article Set citation alerts
    Hongji Zhu, Fengqin Yu. Feature-Weight and Scale Adaptive Algorithm for Kernel Correlation Tracking[J]. Laser & Optoelectronics Progress, 2019, 56(10): 101501 Copy Citation Text show less
    Tracking results obtained using different algorithms in 7 scenarios with different attributes. (a) Doll; (b) Skating 1; (c) Singer 2; (d) Jogging-2; (e) David 3; (f) Basketball; (g) Bolt
    Fig. 1. Tracking results obtained using different algorithms in 7 scenarios with different attributes. (a) Doll; (b) Skating 1; (c) Singer 2; (d) Jogging-2; (e) David 3; (f) Basketball; (g) Bolt
    Results of 51 video sequences. (a) Precision plot; (b) success rate plot
    Fig. 2. Results of 51 video sequences. (a) Precision plot; (b) success rate plot
    Test results of 28 video sequences with scale variation. (a) Precision plot; (b) success rate plot
    Fig. 3. Test results of 28 video sequences with scale variation. (a) Precision plot; (b) success rate plot
    AlgorithmIV (25)SV (28)OCC (29)DEF (19)FM (17)MB (12)OV (6)BC (21)LR (4)IPR (31)OPR (39)
    Proposed0.7480.7330.8340.8140.5610.5480.6170.7330.5840.7640.796
    SAMF0.6820.7030.8200.8100.6080.5640.6360.6760.3850.7140.767
    KCF0.7280.6790.7490.7400.6020.6500.6500.7530.3810.7250.729
    Struck0.5580.6390.5640.5210.6040.5510.5390.5850.5450.6170.597
    CN0.6030.6180.6420.6370.5300.6040.4990.6120.4900.6770.661
    TLD0.5370.6060.5630.5120.5510.5180.5760.4280.3490.5840.596
    CSK0.4810.5030.5000.4760.3810.3420.3790.5850.4110.5470.540
    Table 1. Tracking precision of 7 algorithms in 11 scenarios with different attributes
    AlgorithmIV (25)SV (28)OCC (29)DEF (19)FM (17)MB (12)OV (6)BC (21)LR (4)IPR (31)OPR (39)
    Proposed0.6780.6130.7500.7850.5580.5530.6330.6810.5240.6900.702
    SAMF0.6410.6160.7650.8040.5930.5610.6460.6550.3700.6530.699
    KCF0.5810.4790.6180.6710.5570.5950.6500.6720.3570.6150.608
    Struck0.4910.4710.4930.4730.5670.5180.5500.5450.4100.5280.506
    CN0.4840.4400.5090.5530.5010.5570.4960.5360.4470.5550.524
    TLD0.4600.4940.4680.4560.4730.4820.5160.4910.3270.4760.497
    CSK0.3880.3520.4040.3700.3800.3360.4100.3880.3970.4570.439
    Table 2. Tracking success rates of 7 algorithms in 11 different attribute scenarios