Princeton Tracking Benchmark

Evaluation Result

You can see the detailed evaluation (in .json file) by clicking the title of algorithm. In the ".json" file file, total, type I,II,III are error rate, the successful rate are 1- error rate.

Algorithm Avg.
Rank
target type target size movement oclussion motion type
humananimalrigidlargesmallslowfastyesnopassiveactive
3D-T[1]881.4(1)64.2(13)73.3(16)79.9(1)71.2(10)75.1(15)74.9(4)72.5(1)78.3(13)79.0(10)73.5(4)
ASKCF-OHT[2]18.7351.7(19)49.8(22)72.2(18)58.7(18)59.0(18)67.1(19)55.6(17)52.1(18)68.1(21)71.9(18)54.0(18)
BBT[3]17.5552.0(18)55.3(19)69.4(19)57.7(19)60.1(17)77.1(9)51.9(19)51.9(19)68.9(18)70.4(19)54.8(17)
CSR-rgbd++[4]7.4576.6(5)65.2(9)75.9(12)75.4(7)73.0(7)79.6(5)71.8(7)70.1(5)79.4(10)79.1(9)72.1(6)
CT[5]34.0931.1(34)46.7(30)36.9(37)39.0(32)34.4(36)48.6(34)31.5(35)23.3(38)54.3(30)42.1(35)34.2(34)
DCADCF_colornamejet[6]24.5557.0(16)43.9(31)53.1(32)55.0(21)51.3(26)55.8(29)51.8(20)50.8(20)55.8(29)58.0(26)51.0(20)
DCADCF_fhog_concat[7]25.8249.7(21)48.1(27)53.9(29)51.0(25)50.9(27)51.9(31)50.5(23)48.8(21)53.8(31)58.8(24)47.9(25)
DM-DCF[8]10.4576.0(6)58.0(18)76.7(10)72.4(12)72.8(9)75.2(14)71.6(8)69.1(6)77.5(15)82.5(6)68.9(11)
DS-KCF[9]12.8267.0(11)61.2(15)76.4(11)68.8(14)69.7(13)75.4(13)66.9(13)63.3(12)77.6(14)78.8(11)65.7(14)
DSKCF-CPP[10]12.9164.5(13)64.3(12)74.3(14)66.3(15)69.4(15)76.5(10)64.7(15)60.1(14)79.0(11)79.6(8)63.7(15)
DSKCF_SHAPE[11]10.3670.9(8)70.8(6)73.6(15)73.9(8)70.3(11)76.2(12)70.1(11)64.9(10)81.4(9)77.4(14)69.8(10)
Depth-CCF[12]8.1869.7(10)64.5(11)81.4(3)73.1(9)72.9(8)78.4(8)70.8(10)65.2(9)83.7(8)84.4(2)68.7(12)
Dhog[13f]27.2743.3(26)48.3(26)55.9(24)47.2(28)50.3(28)52.7(30)47.5(26)38.4(28)63.5(25)54.3(32)46.9(27)
ECO_TA[14]5.5576.9(4)64.7(10)79.5(6)77.2(4)74.0(5)78.5(7)74.1(5)68.3(8)85.1(4)83.6(3)72.3(5)
ExampleResult2017-03-051488720222[15]33.6432.9(32)36.3(35)45.2(33)33.8(35)41.4(33)45.8(36)35.1(33)34.1(31)43.7(35)47.4(34)34.6(33)
KCF[16]23.7341.8(27)50.4(21)64.9(21)48.4(26)54.7(22)65.0(21)46.9(27)40.6(26)67.7(22)64.5(22)47.3(26)
KCFmatlab[17]26.8239.7(29)49.4(24)54.6(27)40.1(31)52.5(23)57.8(24)42.9(29)35.2(29)63.6(24)56.4(27)43.7(28)
LDPSTRUCK[18]24.0946.2(24)59.1(16)54.5(28)51.6(24)52.0(25)56.2(28)50.1(24)39.8(27)68.4(19)56.4(28)50.1(22)
LDP_SVT[19]22.8246.8(23)58.8(17)55.8(25)52.9(22)52.3(24)56.3(27)51.1(22)41.0(25)68.4(20)58.2(25)50.4(21)
MIL[20]35.2732.2(33)37.2(34)38.3(36)36.6(34)34.6(35)45.5(37)31.5(36)25.6(36)49.0(33)40.4(38)33.6(36)
OAPF[21]8.4564.2(14)84.8(3)77.2(9)72.7(11)73.4(6)85.1(2)68.4(12)64.4(11)85.1(5)77.7(13)71.4(7)
OF[13g]3917.9(39)11.4(39)23.4(39)20.1(39)17.5(39)18.1(39)18.8(39)15.9(39)22.3(39)23.4(39)16.8(39)
OTR[22]3.9177.3(3)68.3(7)81.3(4)76.5(5)77.3(3)81.2(4)75.3(3)71.3(4)84.7(6)85.1(1)73.9(3)
PCdet[13i]26.3640.6(28)42.1(32)61.7(23)55.4(20)43.6(32)58.5(22)44.8(28)46.3(23)52.0(32)64.9(21)42.6(29)
PCdet_flow[13h]18.2750.5(20)51.6(20)72.7(17)63.4(16)55.5(20)73.9(16)53.0(18)55.0(16)64.4(23)75.5(16)52.7(19)
PCflow[13j]34.3635.2(31)29.1(38)43.6(35)42.2(30)33.2(37)47.2(35)33.1(34)32.4(33)43.5(36)41.3(37)35.5(32)
RGB[13e]32.3626.7(37)40.9(33)54.7(26)31.9(37)46.0(30)50.5(33)35.7(32)34.8(30)46.8(34)56.2(29)33.7(35)
RGBD+OF[13c]13.9163.9(15)65.3(8)74.5(13)71.5(13)65.5(16)73.4(17)65.9(14)60.1(15)79.0(12)74.0(17)65.8(13)
RGBDOcc+OF[13a]8.6474.0(7)62.6(14)78.4(7)78.1(2)69.7(14)76.3(11)72.2(6)72.0(2)75.2(16)82.3(7)70.0(9)
RGBOF[13d]23.7347.1(22)47.0(28)63.6(22)47.4(27)57.5(19)56.7(26)51.8(21)46.9(22)61.9(28)63.4(23)49.3(23)
SAMF+Depth[23]2244.8(25)49.6(23)67.0(20)52.4(23)55.2(21)65.2(20)49.5(25)41.1(24)71.6(17)66.3(20)49.3(24)
SemiB[24]37.6422.5(38)33.0(37)32.7(38)24.0(38)31.6(38)38.2(38)24.4(38)25.1(37)32.7(38)41.9(36)23.2(38)
Struck[25]29.4535.4(30)47.0(29)53.4(31)45.0(29)43.9(31)58.0(23)39.0(30)30.4(34)63.5(26)54.4(31)40.6(30)
TLD[26]34.9129.0(36)35.1(36)44.4(34)32.5(36)38.5(34)51.6(32)29.7(37)33.8(32)38.7(37)50.2(33)30.5(37)
TSDM2019-12-221577069806[27]3.0970.6(9)85.0(2)86.1(1)77.3(3)80.6(2)86.8(1)76.2(2)68.5(7)93.9(1)83.6(4)77.5(2)
VTD[28]30.0930.9(35)48.8(25)53.9(30)38.6(33)46.2(29)57.3(25)37.2(31)28.3(35)63.1(27)54.9(30)38.5(31)
ca3dms+toh[29]6.9166.3(12)74.3(4)82.0(2)73.0(10)74.2(4)79.6(6)71.4(9)63.2(13)88.1(3)82.8(5)70.3(8)
hiob_lc2[30]13.4553.1(17)72.5(5)78.2(8)60.9(17)70.3(12)72.4(18)63.8(16)52.9(17)84.5(7)77.4(15)62.0(16)
zoom2track[31]3.3678.0(2)85.8(1)81.0(5)76.4(6)83.9(1)82.8(3)79.8(1)71.8(3)92.9(2)78.3(12)81.5(1)

References

[1]Adel Bibi, Tianzhu Zhang, and Bernard Ghanem,  3D Part-Based Sparse Tracker with Automatic Synchronization and Registration  Conference on Computer Vision and Pattern Recognition (CVPR2016)
[2]J.T. Pi and K.L. Hu,    
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[7]John Stynsberg,  Masters Thesis  
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13a. RGBD HoG detection+ optical flow + Occlusion handle.
13c. RGBD HoG detection+ optical flow.
13d. RGBD HoG detection.
13e. RGB HoG detection.
13f. D HoG detection.
13g. Optical flow.
13h. Point Cloud (detection+ flow).
13i. Point Cloud detection.
13j. Point Cloud flow.
[14]Yangliu Kuai, Gongjian Wen, Dongdong Li and Jingjing Xiao,  Target-Aware Correlation Filter Tracking in RGBD Videos  
[15]Anonymous,    
[16]Test run by Massimo Camplani, authors are: J. F. Henriques, R. Caseiro, P. Martins, J. Batista,  High-Speed Tracking with Kernelized Correlation Filters  TPAMI 2015
[17]Submitted by Srishti Yadav (tested on MATLAB). Originally by: Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2014),  High-Speed Tracking withKernelized Correlation Filters  IEEE transactions on pattern analysis and machine intelligence
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[27]Pengyao Zhao,  not yet published  
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[29]Ye Liu et al,  Context-aware 3-D Mean-shift with Occlusion Handling for Robust Object Tracking in RGB-D Videos  IEEE Trans. on Multimedia
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[31]Anonymous,