\begin{tabular}{c | c | c | c | c | c | c | c}
{\bf Method} & {\bf MOTA} & {\bf MOTP} & {\bf MT} & {\bf ML} & {\bf IDS} & {\bf FRAG} & {\bf Runtime}\\ \hline
CasTrack \cite{casa2022} & 91.93 \% & 86.19 \% & 86.77 \% & 4.00 \% & 21 & 107 & 0.1 s / 1 core \\
PermaTrack \cite{tokmakov2021learning} & 91.92 \% & 85.83 \% & 86.77 \% & 2.31 \% & 138 & 345 & 0.1 s / GPU \\
CollabMOT \cite{10410636} & 91.88 \% & 85.86 \% & 86.92 \% & 2.46 \% & 248 & 372 & 0.02 s / 4 cores \\
PC-TCNN \cite{wu2021} & 91.75 \% & 86.17 \% & 87.54 \% & 2.92 \% & 26 & 118 & 0.3 s / \\
RAM \cite{tokmakov2022object} & 91.73 \% & 85.90 \% & 87.08 \% & 2.31 \% & 255 & 380 & 0.09 s / GPU \\
Rethink MOT \cite{wang2023mot} & 91.47 \% & 85.63 \% & 89.38 \% & 4.31 \% & 72 & 180 & 0.3 s / 4 cores \\
LEGO \cite{zhang2023lego} & 90.80 \% & 86.75 \% & 87.69 \% & 1.54 \% & 173 & 246 & 0.01 s / 1 core \\
OC-SORT \cite{cao2022observationcentric} & 90.64 \% & 85.71 \% & 81.23 \% & 2.92 \% & 225 & 471 & 0.03 s / 1 core \\
PNAS-MOT \cite{peng2024pnasmot} & 90.42 \% & 85.62 \% & 86.77 \% & 2.31 \% & 552 & 762 & 0.01 s / GPU \\
VirConvTrack \cite{VirConv} & 90.28 \% & 86.93 \% & 83.23 \% & 11.69 \% & 12 & 66 & 0.1 s / 1 core \\
SRK\_ODESA(mc) \cite{ODESA2020} & 90.03 \% & 84.32 \% & 82.62 \% & 2.31 \% & 90 & 501 & 0.4 s / \\
CollabMOT \cite{10410636} & 89.60 \% & 85.04 \% & 82.31 \% & 2.31 \% & 123 & 331 & 0.05 s / 1 core \\
CenterTrack \cite{zhou2020tracking} & 89.44 \% & 85.05 \% & 82.31 \% & 2.31 \% & 116 & 334 & 0.045s / \\
S3Track \cite{s3track} & 88.97 \% & 87.25 \% & 86.92 \% & 1.69 \% & 154 & 369 & 0.03 s / 1 core \\
DEFT \cite{Chaabane2021deft2021CVPRWorkshops} & 88.95 \% & 84.55 \% & 84.77 \% & 1.85 \% & 343 & 553 & 0.04 s / GPU \\
PC3T \cite{wu20213d} & 88.88 \% & 84.37 \% & 80.00 \% & 8.31 \% & 208 & 369 & 0.0045 s / 1 core \\
Mono\_3D\_KF \cite{9626850} & 88.77 \% & 83.95 \% & 80.46 \% & 3.69 \% & 96 & 218 & 0.3 s / 1 core \\
SRK\_ODESA(hc) \cite{ODESA2020} & 88.65 \% & 85.70 \% & 78.92 \% & 2.15 \% & 133 & 582 & 0.4 s / GPU \\
EagerMOT \cite{Kim21ICRA} & 88.21 \% & 85.73 \% & 76.62 \% & 2.46 \% & 121 & 474 & 0.011 s / 4 cores \\
MSA-MOT \cite{s22228650} & 88.19 \% & 85.47 \% & 87.23 \% & 1.23 \% & 56 & 405 & 0.01 s / 1 core \\
UG3DMOT \cite{he20233d} & 88.10 \% & 86.58 \% & 79.23 \% & 5.38 \% & 5 & 330 & 0.1 s / 1 core \\
LGM \cite{wang2021track} & 88.06 \% & 84.16 \% & 85.54 \% & 2.15 \% & 469 & 590 & 0.08 s / GPU \\
TrackMPNN \cite{rangesh2101trackmpnn} & 87.74 \% & 84.55 \% & 84.77 \% & 1.85 \% & 404 & 607 & 0.05 s / 4 cores \\
Stereo3DMOT \cite{mao2023stereo3dmot} & 87.13 \% & 85.17 \% & 75.85 \% & 9.38 \% & 19 & 533 & 0.06 s / 1 core \\
TuSimple \cite{choi2015near} & 86.62 \% & 83.97 \% & 72.46 \% & 6.77 \% & 293 & 501 & 0.6 s / 1 core \\
YONTD-MOTv2 \cite{wang2023you} & 86.57 \% & 86.11 \% & 84.92 \% & 2.00 \% & 54 & 334 & 0.1 s / GPU \\
BcMODT \cite{zhang2023boost} & 86.53 \% & 85.37 \% & 78.31 \% & 2.62 \% & 45 & 626 & 0.01 s / GPU \\
QD-3DT \cite{Hu2021QD3DT} & 86.41 \% & 85.82 \% & 75.38 \% & 2.46 \% & 108 & 553 & 0.03 s / GPU \\
JMODT \cite{huang2021joint} & 86.27 \% & 85.41 \% & 77.38 \% & 2.92 \% & 45 & 585 & 0.01 s / GPU \\
Quasi-Dense \cite{pang2021quasidense} & 85.76 \% & 85.01 \% & 69.08 \% & 3.08 \% & 93 & 617 & 0.07s / \\
JRMOT \cite{Shenoi2020JRMOTAR} & 85.70 \% & 85.48 \% & 71.85 \% & 4.00 \% & 98 & 372 & 0.07 s / 4 cores \\
StrongFusion-MOT \cite{9976946} & 85.63 \% & 85.17 \% & 66.15 \% & 6.00 \% & 34 & 399 & 0.01 s / 8 cores \\
PolarMOT \cite{polarmot} & 85.31 \% & 85.52 \% & 81.38 \% & 2.31 \% & 408 & 900 & 0.02 s / 1 core \\
YONTD-MOT \cite{wang2023you} & 85.19 \% & 87.10 \% & 67.54 \% & 7.08 \% & 21 & 342 & 0.1 s / GPU \\
3DMLA \cite{cho20233d} & 85.12 \% & 84.91 \% & 70.62 \% & 5.85 \% & 15 & 318 & 0.02 s / 1 core \\
EAFFMOT \cite{jin20243d} & 85.04 \% & 85.13 \% & 70.92 \% & 8.31 \% & 15 & 256 & 0.01 s / 1 core \\
MASS \cite{karunasekera2019multiple} & 85.04 \% & 85.53 \% & 74.31 \% & 2.77 \% & 301 & 744 & 0.01s / \\
MOTSFusion \cite{luiten2019MOTSFusion} & 84.83 \% & 85.21 \% & 73.08 \% & 2.77 \% & 275 & 759 & 0.44s / \\
DeepFusion-MOT \cite{9810346} & 84.80 \% & 85.10 \% & 68.46 \% & 9.08 \% & 35 & 444 & 0.01 s / >8 cores \\
mmMOT \cite{mmMOT2019ICCV} & 84.77 \% & 85.21 \% & 73.23 \% & 2.77 \% & 284 & 753 & 0.02s / GPU \\
TripletTrack \cite{Marinello2022CVPR} & 84.77 \% & 86.16 \% & 69.54 \% & 3.38 \% & 222 & 646 & 0.1 s / 1 core \\
FNC2 \cite{Jiang2024Adaptive} & 84.75 \% & 85.80 \% & 76.00 \% & 5.85 \% & 33 & 311 & 0.01 s / 1 core \\
DiTMOT \cite{wang2021ditnet} & 84.73 \% & 84.40 \% & 74.92 \% & 12.92 \% & 31 & 188 & 0.08 s / 1 core \\
mono3DT \cite{Hu3DT19} & 84.52 \% & 85.64 \% & 73.38 \% & 2.77 \% & 377 & 847 & 0.03 s / GPU \\
SMAT \cite{10.100797830305051655} & 84.27 \% & 86.09 \% & 63.08 \% & 5.38 \% & 28 & 341 & 0.1 s / 1 core \\
MOTBeyondPixels \cite{MOTBeyondPixels} & 84.24 \% & 85.73 \% & 73.23 \% & 2.77 \% & 468 & 944 & 0.3 s / 1 core \\
AB3DMOT+PointRCNN \cite{Weng2020AB3DMOT} & 83.92 \% & 85.30 \% & 66.77 \% & 9.08 \% & 10 & 199 & 0.0047s / 1 core \\
MO-YOLO \cite{pan2023mo} & 83.55 \% & 84.61 \% & 72.00 \% & 5.23 \% & 252 & 569 & 0.024 s / \\
IMMDP \cite{Xiang2015ICCV} & 83.04 \% & 82.74 \% & 60.62 \% & 11.38 \% & 172 & 365 & 0.19 s / 4 cores \\
aUToTrack \cite{Burnett2019} & 82.25 \% & 80.52 \% & 72.62 \% & 3.54 \% & 1025 & 1402 & 0.01 s / 1 core \\
JCSTD \cite{Tian2019MOT} & 80.57 \% & 81.81 \% & 56.77 \% & 7.38 \% & 61 & 643 & 0.07 s / 1 core \\
3D-CNN/PMBM \cite{Scheidegger2018} & 80.39 \% & 81.26 \% & 62.77 \% & 6.15 \% & 121 & 613 & 0.01 s / 1 core \\
extraCK \cite{gunduz2018lightweight} & 79.99 \% & 82.46 \% & 62.15 \% & 5.54 \% & 343 & 938 & 0.03 s / 1 core \\
NC2 \cite{10154170} & 78.95 \% & 85.82 \% & 76.00 \% & 5.69 \% & 31 & 275 & 0.01 s / 1 core \\
MCMOT-CPD \cite{Lee2016ECCVWORK} & 78.90 \% & 82.13 \% & 52.31 \% & 11.69 \% & 228 & 536 & 0.01 s / 1 core \\
NOMT* \cite{Choi2015ICCV} & 78.15 \% & 79.46 \% & 57.23 \% & 13.23 \% & 31 & 207 & 0.09 s / 16 cores \\
FANTrack \cite{Baser2019FANTrack3M} & 77.72 \% & 82.33 \% & 62.62 \% & 8.77 \% & 150 & 812 & 0.04 s / 8 cores \\
LP-SSVM* \cite{Wang2016IJCV} & 77.63 \% & 77.80 \% & 56.31 \% & 8.46 \% & 62 & 539 & 0.02 s / 1 core \\
FAMNet \cite{chufamnet} & 77.08 \% & 78.79 \% & 51.38 \% & 8.92 \% & 123 & 713 & 1.5 s / GPU \\
MDP \cite{Xiang2015ICCV} & 76.59 \% & 82.10 \% & 52.15 \% & 13.38 \% & 130 & 387 & 0.9 s / 8 cores \\
DSM \cite{frossardtracking} & 76.15 \% & 83.42 \% & 60.00 \% & 8.31 \% & 296 & 868 & 0.1 s / GPU \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 75.70 \% & 78.46 \% & 58.00 \% & 5.08 \% & 1186 & 2092 & 0.01 a / GPU \\
SCEA* \cite{Yoon2016CVPR} & 75.58 \% & 79.39 \% & 53.08 \% & 11.54 \% & 104 & 448 & 0.06 s / 1 core \\
CIWT* \cite{Osep17ICRA} & 75.39 \% & 79.25 \% & 49.85 \% & 10.31 \% & 165 & 660 & 0.28 s / 1 core \\
NOMT-HM* \cite{Choi2015ICCV} & 75.20 \% & 80.02 \% & 50.00 \% & 13.54 \% & 105 & 351 & 0.09 s / 8 cores \\
SSP* \cite{Lenz2015ICCV} & 72.72 \% & 78.55 \% & 53.85 \% & 8.00 \% & 185 & 932 & 0.6 s / 1 core \\
mbodSSP* \cite{Lenz2015ICCV} & 72.69 \% & 78.75 \% & 48.77 \% & 8.77 \% & 114 & 858 & 0.01 s / 1 core \\
SASN-MCF\_nano \cite{gunduz2019efficient} & 70.86 \% & 82.65 \% & 58.00 \% & 7.85 \% & 443 & 975 & 0.02 s / 1 core \\
Point3DT \cite{PointTrackNet} & 68.24 \% & 76.57 \% & 60.62 \% & 12.31 \% & 111 & 725 & 0.05 s / 1 core \\
DCO-X* \cite{Milan2013CVPR} & 68.11 \% & 78.85 \% & 37.54 \% & 14.15 \% & 318 & 959 & 0.9 s / 1 core \\
NOMT \cite{Choi2015ICCV} & 66.60 \% & 78.17 \% & 41.08 \% & 25.23 \% & 13 & 150 & 0.09 s / 16 core \\
RMOT* \cite{Yoon2015WACV} & 65.83 \% & 75.42 \% & 40.15 \% & 9.69 \% & 209 & 727 & 0.02 s / 1 core \\
LP-SSVM \cite{Wang2016IJCV} & 61.77 \% & 76.93 \% & 35.54 \% & 21.69 \% & 16 & 422 & 0.05 s / 1 core \\
NOMT-HM \cite{Choi2015ICCV} & 61.17 \% & 78.65 \% & 33.85 \% & 28.00 \% & 28 & 241 & 0.09 s / 8 cores \\
ODAMOT \cite{Gaidon2015BMVC} & 59.23 \% & 75.45 \% & 27.08 \% & 15.54 \% & 389 & 1274 & 1 s / 1 core \\
SSP \cite{Lenz2015ICCV} & 57.85 \% & 77.64 \% & 29.38 \% & 24.31 \% & 7 & 704 & 0.6s / 1 core \\
SCEA \cite{Yoon2016CVPR} & 57.03 \% & 78.84 \% & 26.92 \% & 26.62 \% & 17 & 461 & 0.05 s / 1 core \\
mbodSSP \cite{Lenz2015ICCV} & 56.03 \% & 77.52 \% & 23.23 \% & 27.23 \% & 0 & 699 & 0.01 s / 1 core \\
TBD \cite{Geiger2014PAMI} & 55.07 \% & 78.35 \% & 20.46 \% & 32.62 \% & 31 & 529 & 10 s / 1 core \\
SORT \cite{bewley2016simple} & 54.22 \% & 77.57 \% & 25.69 \% & 29.08 \% & 1 & 557 & .002 s / 1 core \\
RMOT \cite{Yoon2015WACV} & 52.42 \% & 75.18 \% & 21.69 \% & 31.85 \% & 50 & 376 & 0.01 s / 1 core \\
CEM \cite{Milan2014PAMI} & 51.94 \% & 77.11 \% & 20.00 \% & 31.54 \% & 125 & 396 & 0.09 s / 1 core \\
MCF \cite{Zhang2008CVPR} & 45.92 \% & 78.25 \% & 14.92 \% & 37.23 \% & 21 & 581 & 0.01 s / 1 core \\
HM \cite{Geiger2013} & 43.85 \% & 78.34 \% & 12.46 \% & 39.54 \% & 12 & 571 & 0.01 s / 1 core \\
DP-MCF \cite{Pirsiavash2011CVPR} & 38.33 \% & 78.41 \% & 18.00 \% & 36.15 \% & 2716 & 3225 & 0.01 s / 1 core \\
DCO \cite{Andriyenko2012CVPR} & 37.28 \% & 74.36 \% & 15.54 \% & 30.92 \% & 220 & 612 & 0.03 s / 1 core \\
FMMOVT \cite{Alencar2015LARS} & 31.88 \% & 77.68 \% & 21.38 \% & 34.92 \% & 511 & 930 & 0.05 s / 1 core
\end{tabular}