Benchmarking SAM2-based trackers on FMOX

Data and R code for some figures in AICS 2025

Author

Affiliation

Rozenn Dahyot and Senem Aktas

Maynooth University

Published

December 12, 2025

Introduction

This is supplementary material to our paper published at AICS 2025 with the data and R code used for creating some figures in the paper.

R code

DataFrame

library(ggplot2)
library(knitr)


# Falling object : 6 sequences
datasetname0=rep(c("Falling_Object"),each=6*4)

trackername0=rep(c("SAM2","EfficientTAM","DAM4SAM","SAMURAI"),each=6)

sequence0=rep(c("v_box_GTgamma",
            "v_cell_GTgamma",
            "v_key_GTgamma",
            "v_marker_GTgamma",
            "v_pen_GTgamma",
            "v_rubber_GTgamma"),times=4)


mIoU0=c(0.727,0.531,0.501,0.534, 0.610,0.471,
        0.722,0.460,0.456,0.525,0.441,0.405,
        0.718,0.525, 0.536,0.525, 0.637, 0.484,
        0.664, 0.446, 0.479, 0.542, 0.519, 0.470)

mDice0=c(0.836,0.669,0.621,0.672,0.745,0.610,
         0.832, 0.590,0.570,0.655,0.525,0.506,
         0.829, 0.663, 0.664, 0.664,0.771, 0.622,
         0.775, 0.577, 0.600, 0.679, 0.642, 0.603)

mOverlap0=rep(c(0.436,0.257,0.245,0.308,0.401,0.317),times=4)
mObjSize0=rep(c(3.000,3.000,3.000,3.000,3.667,3.000),times=4)



# TbD-3D: 10 sequences
datasetname1=rep(c("TbD-3D"),each=10*4)
trackername1=rep(c("SAM2","EfficientTAM","DAM4SAM","SAMURAI"),each=10)

sequence1=rep(c("HighFPS_GT_depth2",
            "HighFPS_GT_depthb2",
            "HighFPS_GT_depthf1",
            "HighFPS_GT_depthf2",
            "HighFPS_GT_depthf3",
            "HighFPS_GT_out1",
            "HighFPS_GT_out2",
            "HighFPS_GT_outa1",
            "HighFPS_GT_outb1",
            "HighFPS_GT_outf1"),times=4)

mOverlap1=rep(c(0.627,0.699,0.541,0.651,0.646,0.405,0.373,0.464,0.318,0.396),times=4)

mObjSize1=rep(c(3.043,3.000,3.000,3.000,3.000,3.018,3.000,3.304,3.000,3.000),times=4)


mIoU1=c(0.726,0.598,0.668,0.635,0.646,0.747,0.737,0.724,0.608,0.685,
        0.693,0.568,0.618,0.616,0.602,0.737,0.722,0.703,0.554,0.673,
        0.712,0.593,0.666,0.640,0.640,0.734,0.722,0.692,0.596,0.678,
        0.701,0.587,0.632,0.615,0.656,0.734,0.722,0.702,0.623,0.658)

mDice1=c(0.833,0.747,0.795,0.774,0.781,0.853,0.847,0.838, 0.745,0.810,
         0.811,0.723,0.759,0.760,0.746,0.847,0.837,0.824,0.702,0.802,
         0.823,0.743,0.794,0.777,0.777,0.845,0.837,0.816,0.737,0.806,
         0.817,0.738,0.767,0.758,0.789,0.845,0.836,0.822,0.752,0.790)


# FMOv2 : 18 sequences
datasetname2=rep(c("FMOv2"),each=18*4)
trackername2=rep(c("SAM2","EfficientTAM","DAM4SAM","SAMURAI"),each=18)

sequence2=rep(c("atp_serves",
            "blue",
            "darts1",
            "darts_window1",
            "frisbee",
            "hockey",
            "ping_pong_paint",
            "ping_pong_side",
            "ping_pong_top",
            "softball",
            "squash",
            "tennis1",
            "tennis2",
            "tennis_serve_back",
            "tennis_serve_side",
            "volleyball1",
            "volleyball_passing",
            "william_tell"),times=4)

mOverlap2=rep(c(0.000,0.056,0.007,0.000,0.036,0.000,0.050,0.035,0.245,0.023,0.002,0.061,0.145,0.132,0.011,0.123,0.235,0.248),times=4)
  
mObjSize2=rep(c(1.169,3.000,3.086,3.000,3.842,0.339,2.905,2.248,1.985,2.444,0.046,0.019,0.339,0.655,1.824,3.917,3.046,3.000),times=4)

mIoU2=c(0.035,0.705,0.647,0.000,0.928,0.082,0.024,0.657,0.632,0.000,0.024,0.287,0.032,0.566,0.721,0.830,0.780,0.585,
        0.116,0.711,0.648,0.000,0.488,0.387,0.026,0.611,0.381,0.000,0.023,0.112,0.005,0.271,0.710,0.799,0.775,0.668,
        0.337,0.707,0.632,0.039,0.761,0.088,0.058,0.704,0.785,0.000,0.024,0.292,0.024,0.559,0.755,0.819,0.766,0.598,
        0.288,0.712,0.658,0.183,0.925,0.079,0.357,0.668,0.783,0.001,0.024,0.304,0.027,0.589,0.730,0.825,0.781,0.632)

mDice2=c(0.038,0.809,0.780,0.000,0.962,0.096,0.029,0.739,0.698,0.000,0.028,0.388,0.047,0.668,0.814,0.906,0.874,0.701,
         0.134,0.790,0.774,0.000,0.506,0.486,0.032,0.678,0.426,0.000,0.028,0.149,0.007,0.313, 0.805,0.885,0.870,0.762,
         0.380,0.811,0.766,0.068,0.812,0.108,0.067,0.791,0.865,0.000,0.029,0.396,0.034,0.664,0.855,0.899,0.865,0.712,
         0.327,0.814,0.785,0.211,0.961,0.093,0.405,0.748, 0.868,0.002, 0.029,0.410,0.039,0.699,0.838,0.903,0.874,0.733)



# TbD : 12 sequences
datasetname3=rep(c("TbD"),each=12*4)
trackername3=rep(c("SAM2","EfficientTAM","DAM4SAM","SAMURAI"),each=12)
sequence3=rep(c("VS_badminton_white_GX010058-8",
            "VS_badminton_yellow_GX010060-8",  
            "fall_cube",
            "hit_tennis",
            "hit_tennis2",
            "VS_pingpong_GX010051-8",
            "VS_roll_golf-gc-12",
            "VS_tennis_GX010073-8",
            "throw_floor",
            "throw_soft",
            "throw_tennis",
            "VS_volleyball_GX010068-12"),times=4)

mOverlap3=rep(c(0.094,0.084,0.376,0.401,0.311,0.091,0.321,0.123,0.330,0.373,0.232,0.262),times=4)

mObjSize3=rep(c(2.487,2.179,3.158,3.000,3.160,2.474,0.321,2.784,3.205,3.119,3.227,2.925),times=4)

mIoU3=c(0.002,0.456,0.552,0.617,0.064,0.572,0.620,0.001,0.001,0.001,0.001,0.636,
        0.001,0.226,0.542,0.618,0.064,0.610,0.614,0.589,0.000,0.001,0.000,0.641,
        0.625,0.474,0.549,0.617,0.422,0.550,0.612,0.010,0.011,0.001,0.670,0.634,
        0.002,0.287,0.546,0.613,0.168,0.622,0.603,0.005,0.040,0.513,0.068,0.649)

mDice3=c(0.004,0.571,0.698, 0.761,0.081,0.675,0.764,0.001,0.002,0.001, 0.002, 0.764,
         0.002,0.276,0.691,0.764,0.081,0.730,0.759,0.717,0.000,0.001, 0.000, 0.768,
         0.745,0.591,0.698,0.761,0.517,0.655,0.758,0.014,0.015,0.001,0.798,0.762,
         0.004,0.352,0.698,0.759,0.207,0.744,0.751,0.010,0.064,0.654,0.118,0.767)


# create a data frame
datasetname=c(datasetname0,datasetname1,datasetname2,datasetname3)
Tracker=c(trackername0,trackername1,trackername2,trackername3)
sequence=c(sequence0,sequence1,sequence2,sequence3)

mOverlap=c(mOverlap0,mOverlap1,mOverlap2,mOverlap3)
mObjSize=c(mObjSize0,mObjSize1,mObjSize2,mObjSize3)

mIoU=c(mIoU0,mIoU1,mIoU2,mIoU3)
mDice=c(mDice0,mDice1,mDice2,mDice3)  

data=data.frame(datasetname,sequence, Tracker,  mIoU,mDice,mOverlap,mObjSize)

Fig. 1

ggplot(data, aes(x=mObjSize, y=mOverlap,  color=datasetname)) +
  geom_point(size=3)+
  xlab("Object size category")+
  ylab("Relative BB overlaps")+
  labs(color="Datasets")+
  theme_bw()

Fig. 2 (left)

ggplot(data, aes(x=datasetname, y=mIoU, fill=Tracker)) + 
  geom_boxplot()+
  xlab("Dataset names")+
  theme_bw()

Fig. 2 (right)

ggplot(data, aes(x=datasetname, y=mDice, fill=Tracker)) + 
  geom_boxplot()+
  xlab("Dataset names")+
  theme_bw()

Tab. 1

library(dplyr)

summary_mIoU <- data %>%
  group_by(Tracker) %>%
  summarise(
    MIN_mIoU= min(mIoU),
    MAX_mIoU= max(mIoU),
    MEDIAN_mIoU = median(mIoU),
    MEAN_mIoU = mean(mIoU)
  )


summary_mDice <- data %>%
  group_by(Tracker) %>%
  summarise(
    MIN_mDice= min(mDice),
    MAX_mDice= max(mDice),
    MEDIAN_mDice = median(mDice),
    MEAN_mDice = mean(mDice)
  )

kable(summary_mIoU)

Tracker	MIN_mIoU	MAX_mIoU	MEDIAN_mIoU	MEAN_mIoU
DAM4SAM	0.000	0.819	0.6050	0.5048043
EfficientTAM	0.000	0.799	0.5480	0.4376522
SAM2	0.000	0.928	0.5915	0.4610000
SAMURAI	0.001	0.925	0.5960	0.4876522

kable(summary_mDice)

Tracker	MIN_mDice	MAX_mDice	MEDIAN_mDice	MEAN_mDice
DAM4SAM	0.000	0.899	0.7440	0.6001087
EfficientTAM	0.000	0.885	0.6845	0.5200652
SAM2	0.000	0.962	0.6995	0.5451522
SAMURAI	0.002	0.961	0.7355	0.5795000

Data

Print out of the dataframe:

  kable(data)

datasetname	sequence	Tracker	mIoU	mDice	mOverlap	mObjSize
Falling_Object	v_box_GTgamma	SAM2	0.727	0.836	0.436	3.000
Falling_Object	v_cell_GTgamma	SAM2	0.531	0.669	0.257	3.000
Falling_Object	v_key_GTgamma	SAM2	0.501	0.621	0.245	3.000
Falling_Object	v_marker_GTgamma	SAM2	0.534	0.672	0.308	3.000
Falling_Object	v_pen_GTgamma	SAM2	0.610	0.745	0.401	3.667
Falling_Object	v_rubber_GTgamma	SAM2	0.471	0.610	0.317	3.000
Falling_Object	v_box_GTgamma	EfficientTAM	0.722	0.832	0.436	3.000
Falling_Object	v_cell_GTgamma	EfficientTAM	0.460	0.590	0.257	3.000
Falling_Object	v_key_GTgamma	EfficientTAM	0.456	0.570	0.245	3.000
Falling_Object	v_marker_GTgamma	EfficientTAM	0.525	0.655	0.308	3.000
Falling_Object	v_pen_GTgamma	EfficientTAM	0.441	0.525	0.401	3.667
Falling_Object	v_rubber_GTgamma	EfficientTAM	0.405	0.506	0.317	3.000
Falling_Object	v_box_GTgamma	DAM4SAM	0.718	0.829	0.436	3.000
Falling_Object	v_cell_GTgamma	DAM4SAM	0.525	0.663	0.257	3.000
Falling_Object	v_key_GTgamma	DAM4SAM	0.536	0.664	0.245	3.000
Falling_Object	v_marker_GTgamma	DAM4SAM	0.525	0.664	0.308	3.000
Falling_Object	v_pen_GTgamma	DAM4SAM	0.637	0.771	0.401	3.667
Falling_Object	v_rubber_GTgamma	DAM4SAM	0.484	0.622	0.317	3.000
Falling_Object	v_box_GTgamma	SAMURAI	0.664	0.775	0.436	3.000
Falling_Object	v_cell_GTgamma	SAMURAI	0.446	0.577	0.257	3.000
Falling_Object	v_key_GTgamma	SAMURAI	0.479	0.600	0.245	3.000
Falling_Object	v_marker_GTgamma	SAMURAI	0.542	0.679	0.308	3.000
Falling_Object	v_pen_GTgamma	SAMURAI	0.519	0.642	0.401	3.667
Falling_Object	v_rubber_GTgamma	SAMURAI	0.470	0.603	0.317	3.000
TbD-3D	HighFPS_GT_depth2	SAM2	0.726	0.833	0.627	3.043
TbD-3D	HighFPS_GT_depthb2	SAM2	0.598	0.747	0.699	3.000
TbD-3D	HighFPS_GT_depthf1	SAM2	0.668	0.795	0.541	3.000
TbD-3D	HighFPS_GT_depthf2	SAM2	0.635	0.774	0.651	3.000
TbD-3D	HighFPS_GT_depthf3	SAM2	0.646	0.781	0.646	3.000
TbD-3D	HighFPS_GT_out1	SAM2	0.747	0.853	0.405	3.018
TbD-3D	HighFPS_GT_out2	SAM2	0.737	0.847	0.373	3.000
TbD-3D	HighFPS_GT_outa1	SAM2	0.724	0.838	0.464	3.304
TbD-3D	HighFPS_GT_outb1	SAM2	0.608	0.745	0.318	3.000
TbD-3D	HighFPS_GT_outf1	SAM2	0.685	0.810	0.396	3.000
TbD-3D	HighFPS_GT_depth2	EfficientTAM	0.693	0.811	0.627	3.043
TbD-3D	HighFPS_GT_depthb2	EfficientTAM	0.568	0.723	0.699	3.000
TbD-3D	HighFPS_GT_depthf1	EfficientTAM	0.618	0.759	0.541	3.000
TbD-3D	HighFPS_GT_depthf2	EfficientTAM	0.616	0.760	0.651	3.000
TbD-3D	HighFPS_GT_depthf3	EfficientTAM	0.602	0.746	0.646	3.000
TbD-3D	HighFPS_GT_out1	EfficientTAM	0.737	0.847	0.405	3.018
TbD-3D	HighFPS_GT_out2	EfficientTAM	0.722	0.837	0.373	3.000
TbD-3D	HighFPS_GT_outa1	EfficientTAM	0.703	0.824	0.464	3.304
TbD-3D	HighFPS_GT_outb1	EfficientTAM	0.554	0.702	0.318	3.000
TbD-3D	HighFPS_GT_outf1	EfficientTAM	0.673	0.802	0.396	3.000
TbD-3D	HighFPS_GT_depth2	DAM4SAM	0.712	0.823	0.627	3.043
TbD-3D	HighFPS_GT_depthb2	DAM4SAM	0.593	0.743	0.699	3.000
TbD-3D	HighFPS_GT_depthf1	DAM4SAM	0.666	0.794	0.541	3.000
TbD-3D	HighFPS_GT_depthf2	DAM4SAM	0.640	0.777	0.651	3.000
TbD-3D	HighFPS_GT_depthf3	DAM4SAM	0.640	0.777	0.646	3.000
TbD-3D	HighFPS_GT_out1	DAM4SAM	0.734	0.845	0.405	3.018
TbD-3D	HighFPS_GT_out2	DAM4SAM	0.722	0.837	0.373	3.000
TbD-3D	HighFPS_GT_outa1	DAM4SAM	0.692	0.816	0.464	3.304
TbD-3D	HighFPS_GT_outb1	DAM4SAM	0.596	0.737	0.318	3.000
TbD-3D	HighFPS_GT_outf1	DAM4SAM	0.678	0.806	0.396	3.000
TbD-3D	HighFPS_GT_depth2	SAMURAI	0.701	0.817	0.627	3.043
TbD-3D	HighFPS_GT_depthb2	SAMURAI	0.587	0.738	0.699	3.000
TbD-3D	HighFPS_GT_depthf1	SAMURAI	0.632	0.767	0.541	3.000
TbD-3D	HighFPS_GT_depthf2	SAMURAI	0.615	0.758	0.651	3.000
TbD-3D	HighFPS_GT_depthf3	SAMURAI	0.656	0.789	0.646	3.000
TbD-3D	HighFPS_GT_out1	SAMURAI	0.734	0.845	0.405	3.018
TbD-3D	HighFPS_GT_out2	SAMURAI	0.722	0.836	0.373	3.000
TbD-3D	HighFPS_GT_outa1	SAMURAI	0.702	0.822	0.464	3.304
TbD-3D	HighFPS_GT_outb1	SAMURAI	0.623	0.752	0.318	3.000
TbD-3D	HighFPS_GT_outf1	SAMURAI	0.658	0.790	0.396	3.000
FMOv2	atp_serves	SAM2	0.035	0.038	0.000	1.169
FMOv2	blue	SAM2	0.705	0.809	0.056	3.000
FMOv2	darts1	SAM2	0.647	0.780	0.007	3.086
FMOv2	darts_window1	SAM2	0.000	0.000	0.000	3.000
FMOv2	frisbee	SAM2	0.928	0.962	0.036	3.842
FMOv2	hockey	SAM2	0.082	0.096	0.000	0.339
FMOv2	ping_pong_paint	SAM2	0.024	0.029	0.050	2.905
FMOv2	ping_pong_side	SAM2	0.657	0.739	0.035	2.248
FMOv2	ping_pong_top	SAM2	0.632	0.698	0.245	1.985
FMOv2	softball	SAM2	0.000	0.000	0.023	2.444
FMOv2	squash	SAM2	0.024	0.028	0.002	0.046
FMOv2	tennis1	SAM2	0.287	0.388	0.061	0.019
FMOv2	tennis2	SAM2	0.032	0.047	0.145	0.339
FMOv2	tennis_serve_back	SAM2	0.566	0.668	0.132	0.655
FMOv2	tennis_serve_side	SAM2	0.721	0.814	0.011	1.824
FMOv2	volleyball1	SAM2	0.830	0.906	0.123	3.917
FMOv2	volleyball_passing	SAM2	0.780	0.874	0.235	3.046
FMOv2	william_tell	SAM2	0.585	0.701	0.248	3.000
FMOv2	atp_serves	EfficientTAM	0.116	0.134	0.000	1.169
FMOv2	blue	EfficientTAM	0.711	0.790	0.056	3.000
FMOv2	darts1	EfficientTAM	0.648	0.774	0.007	3.086
FMOv2	darts_window1	EfficientTAM	0.000	0.000	0.000	3.000
FMOv2	frisbee	EfficientTAM	0.488	0.506	0.036	3.842
FMOv2	hockey	EfficientTAM	0.387	0.486	0.000	0.339
FMOv2	ping_pong_paint	EfficientTAM	0.026	0.032	0.050	2.905
FMOv2	ping_pong_side	EfficientTAM	0.611	0.678	0.035	2.248
FMOv2	ping_pong_top	EfficientTAM	0.381	0.426	0.245	1.985
FMOv2	softball	EfficientTAM	0.000	0.000	0.023	2.444
FMOv2	squash	EfficientTAM	0.023	0.028	0.002	0.046
FMOv2	tennis1	EfficientTAM	0.112	0.149	0.061	0.019
FMOv2	tennis2	EfficientTAM	0.005	0.007	0.145	0.339
FMOv2	tennis_serve_back	EfficientTAM	0.271	0.313	0.132	0.655
FMOv2	tennis_serve_side	EfficientTAM	0.710	0.805	0.011	1.824
FMOv2	volleyball1	EfficientTAM	0.799	0.885	0.123	3.917
FMOv2	volleyball_passing	EfficientTAM	0.775	0.870	0.235	3.046
FMOv2	william_tell	EfficientTAM	0.668	0.762	0.248	3.000
FMOv2	atp_serves	DAM4SAM	0.337	0.380	0.000	1.169
FMOv2	blue	DAM4SAM	0.707	0.811	0.056	3.000
FMOv2	darts1	DAM4SAM	0.632	0.766	0.007	3.086
FMOv2	darts_window1	DAM4SAM	0.039	0.068	0.000	3.000
FMOv2	frisbee	DAM4SAM	0.761	0.812	0.036	3.842
FMOv2	hockey	DAM4SAM	0.088	0.108	0.000	0.339
FMOv2	ping_pong_paint	DAM4SAM	0.058	0.067	0.050	2.905
FMOv2	ping_pong_side	DAM4SAM	0.704	0.791	0.035	2.248
FMOv2	ping_pong_top	DAM4SAM	0.785	0.865	0.245	1.985
FMOv2	softball	DAM4SAM	0.000	0.000	0.023	2.444
FMOv2	squash	DAM4SAM	0.024	0.029	0.002	0.046
FMOv2	tennis1	DAM4SAM	0.292	0.396	0.061	0.019
FMOv2	tennis2	DAM4SAM	0.024	0.034	0.145	0.339
FMOv2	tennis_serve_back	DAM4SAM	0.559	0.664	0.132	0.655
FMOv2	tennis_serve_side	DAM4SAM	0.755	0.855	0.011	1.824
FMOv2	volleyball1	DAM4SAM	0.819	0.899	0.123	3.917
FMOv2	volleyball_passing	DAM4SAM	0.766	0.865	0.235	3.046
FMOv2	william_tell	DAM4SAM	0.598	0.712	0.248	3.000
FMOv2	atp_serves	SAMURAI	0.288	0.327	0.000	1.169
FMOv2	blue	SAMURAI	0.712	0.814	0.056	3.000
FMOv2	darts1	SAMURAI	0.658	0.785	0.007	3.086
FMOv2	darts_window1	SAMURAI	0.183	0.211	0.000	3.000
FMOv2	frisbee	SAMURAI	0.925	0.961	0.036	3.842
FMOv2	hockey	SAMURAI	0.079	0.093	0.000	0.339
FMOv2	ping_pong_paint	SAMURAI	0.357	0.405	0.050	2.905
FMOv2	ping_pong_side	SAMURAI	0.668	0.748	0.035	2.248
FMOv2	ping_pong_top	SAMURAI	0.783	0.868	0.245	1.985
FMOv2	softball	SAMURAI	0.001	0.002	0.023	2.444
FMOv2	squash	SAMURAI	0.024	0.029	0.002	0.046
FMOv2	tennis1	SAMURAI	0.304	0.410	0.061	0.019
FMOv2	tennis2	SAMURAI	0.027	0.039	0.145	0.339
FMOv2	tennis_serve_back	SAMURAI	0.589	0.699	0.132	0.655
FMOv2	tennis_serve_side	SAMURAI	0.730	0.838	0.011	1.824
FMOv2	volleyball1	SAMURAI	0.825	0.903	0.123	3.917
FMOv2	volleyball_passing	SAMURAI	0.781	0.874	0.235	3.046
FMOv2	william_tell	SAMURAI	0.632	0.733	0.248	3.000
TbD	VS_badminton_white_GX010058-8	SAM2	0.002	0.004	0.094	2.487
TbD	VS_badminton_yellow_GX010060-8	SAM2	0.456	0.571	0.084	2.179
TbD	fall_cube	SAM2	0.552	0.698	0.376	3.158
TbD	hit_tennis	SAM2	0.617	0.761	0.401	3.000
TbD	hit_tennis2	SAM2	0.064	0.081	0.311	3.160
TbD	VS_pingpong_GX010051-8	SAM2	0.572	0.675	0.091	2.474
TbD	VS_roll_golf-gc-12	SAM2	0.620	0.764	0.321	0.321
TbD	VS_tennis_GX010073-8	SAM2	0.001	0.001	0.123	2.784
TbD	throw_floor	SAM2	0.001	0.002	0.330	3.205
TbD	throw_soft	SAM2	0.001	0.001	0.373	3.119
TbD	throw_tennis	SAM2	0.001	0.002	0.232	3.227
TbD	VS_volleyball_GX010068-12	SAM2	0.636	0.764	0.262	2.925
TbD	VS_badminton_white_GX010058-8	EfficientTAM	0.001	0.002	0.094	2.487
TbD	VS_badminton_yellow_GX010060-8	EfficientTAM	0.226	0.276	0.084	2.179
TbD	fall_cube	EfficientTAM	0.542	0.691	0.376	3.158
TbD	hit_tennis	EfficientTAM	0.618	0.764	0.401	3.000
TbD	hit_tennis2	EfficientTAM	0.064	0.081	0.311	3.160
TbD	VS_pingpong_GX010051-8	EfficientTAM	0.610	0.730	0.091	2.474
TbD	VS_roll_golf-gc-12	EfficientTAM	0.614	0.759	0.321	0.321
TbD	VS_tennis_GX010073-8	EfficientTAM	0.589	0.717	0.123	2.784
TbD	throw_floor	EfficientTAM	0.000	0.000	0.330	3.205
TbD	throw_soft	EfficientTAM	0.001	0.001	0.373	3.119
TbD	throw_tennis	EfficientTAM	0.000	0.000	0.232	3.227
TbD	VS_volleyball_GX010068-12	EfficientTAM	0.641	0.768	0.262	2.925
TbD	VS_badminton_white_GX010058-8	DAM4SAM	0.625	0.745	0.094	2.487
TbD	VS_badminton_yellow_GX010060-8	DAM4SAM	0.474	0.591	0.084	2.179
TbD	fall_cube	DAM4SAM	0.549	0.698	0.376	3.158
TbD	hit_tennis	DAM4SAM	0.617	0.761	0.401	3.000
TbD	hit_tennis2	DAM4SAM	0.422	0.517	0.311	3.160
TbD	VS_pingpong_GX010051-8	DAM4SAM	0.550	0.655	0.091	2.474
TbD	VS_roll_golf-gc-12	DAM4SAM	0.612	0.758	0.321	0.321
TbD	VS_tennis_GX010073-8	DAM4SAM	0.010	0.014	0.123	2.784
TbD	throw_floor	DAM4SAM	0.011	0.015	0.330	3.205
TbD	throw_soft	DAM4SAM	0.001	0.001	0.373	3.119
TbD	throw_tennis	DAM4SAM	0.670	0.798	0.232	3.227
TbD	VS_volleyball_GX010068-12	DAM4SAM	0.634	0.762	0.262	2.925
TbD	VS_badminton_white_GX010058-8	SAMURAI	0.002	0.004	0.094	2.487
TbD	VS_badminton_yellow_GX010060-8	SAMURAI	0.287	0.352	0.084	2.179
TbD	fall_cube	SAMURAI	0.546	0.698	0.376	3.158
TbD	hit_tennis	SAMURAI	0.613	0.759	0.401	3.000
TbD	hit_tennis2	SAMURAI	0.168	0.207	0.311	3.160
TbD	VS_pingpong_GX010051-8	SAMURAI	0.622	0.744	0.091	2.474
TbD	VS_roll_golf-gc-12	SAMURAI	0.603	0.751	0.321	0.321
TbD	VS_tennis_GX010073-8	SAMURAI	0.005	0.010	0.123	2.784
TbD	throw_floor	SAMURAI	0.040	0.064	0.330	3.205
TbD	throw_soft	SAMURAI	0.513	0.654	0.373	3.119
TbD	throw_tennis	SAMURAI	0.068	0.118	0.232	3.227
TbD	VS_volleyball_GX010068-12	SAMURAI	0.649	0.767	0.262	2.925