# Multiple Object Tracking in Robotic Applications: Trends and Challenges
## 4 Evaluation and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Dataset** & **Tracker** & **MOTA \(\uparrow\)** & **MOTp \(\uparrow\)** & **IDPI \(\uparrow\)** & **MT \(\uparrow\)** & **ML \(\downarrow\)** & **FP \(\downarrow\)** & **FN \(\downarrow\)** & **IDS \(\downarrow\)** & **Frag \(\downarrow\)** & **Hz \(\uparrow\)** & **HOTA \(\uparrow\)** & **Protocol** \\ \hline  & [PERSON] et al. [48] & 53.0 & **75.5** & 29.1 & 20.2 & 5159 & 22,984 & 708 & 1476 & & Private \\  & [PERSON] et al. [48] & 38.5 & 72.6 & 8.7 & 37.4 & 4005 & 33,204 & 586 & 1263 & & Public \\  & [PERSON] et al. [31] & 37.6 & 71.7 & 15.8 & 26.8 & 7933 & 29,397 & 1026 & 2024 & 1 & Public \\  & [PERSON] et al. [37] & 35.6 & 45.1 & 23.2 & 39.3 & 10,580 & 28,508 & 457 & **969** & & Private \\  & [PERSON] et al. [38] & **56.5** & 73.0 & **61.3** & **45.1** & **14.6** & 9386 & 16,921 & **428** & 1364 & 5.1 & Public \\  & [PERSON] et al. [32] & 37.1 & 72.5 & 28.4 & 12.6 & 39.7 & 8305 & 29,732 & 580 & 1193 & 1.0 & Private \\  & [PERSON] et al. [39] & 38.9 & 70.6 & 44.5 & 16.6 & 31.5 & 7321 & 29,501 & 720 & 1440 & 0.3 & Public \\  & [PERSON] et al. [42] & 38.3 & 71.1 & 45.6 & 17.6 & 41.2 & **1290** & **2700** & 1648 & 1515 & 6.3 & Public \\  & [PERSON] et al. [46] & 30.5 & 71.2 & 1.1 & 7.6 & 41.2 & 6534 & 35,284 & 879 & 2208 & 5.9 & Private \\  & [PERSON] et al. [80] & 24.2 & 70.2 & & 7.1 & 49.2 & 6400 & 39,659 & 529 & 1034 & & Private \\ \[PERSON]  & [PERSON] et al. [49] & 22.5 & 70.9 & 25.9 & 6.4 & 61.9 & 7346 & 39,092 & 1159 & 1538 & **172.8** & Public \\  & [PERSON] et al. [31] & 47.2 & & **75.8** & 14.0 & 41.6 & 2681 & 92,856 & 774 & 1675 & 1.0 & Public \\  & [PERSON] et al. [37] & 47.1 & & 52.3 & 20.4 & 46.9 & 6703 & 89,368 & **370** & **598** & & Private \\  & [PERSON] et al. [38] & 63 & 78.8 & & 39.9 & 22.1 & 13,663 & 53,248 & 482 & 1251 & & Public \\  & [PERSON] et al. [32] & 48.3 & 76.7 & 15.4 & 40.1 & 2706 & 91,047 & 543 & & 0.5 & Private \\  & [PERSON] et al. [40] & 46.1 & 73.8 & 54.8 & 17.4 & 42.7 & 7909 & 89,874 & 532 & 1616 & 0.3 & Private \\  & [PERSON] et al. [41] & 64.8 & 78.6 & **73.5** & **40.6** & 22.0 & 13,470 & 49,927 & 794 & 1050 & 18.2 & Private \\  & [PERSON] et al. [39] & 48.8 & 75.7 & 47.2 & 15.8 & 38.1 & 5875 & 86,567 & 906 & 1116 & 0.1 & Public \\  & [PERSON] et al. [43] & 67.6 & 78.4 & 57.2 & 32.9 & 23.1 & 8934 & 48,305 & 1897 & 34.4 & Private \\ MOT16 & [PERSON] et al. [100] & 47.8 & & 47.8 & 19.1 & 38.2 & 8866 & 58,487 & 852 & & Private \\  & [PERSON] et al. [44] & 49.2 & & 56.1 & 17.3 & 40.3 & 8400 & 83,702 & & 882 & & Public \\  & [PERSON] et al. [34] & 65.2 & 78.4 & & 32.4 & 21.3 & 6578 & 58,599 & 946 & 2283 & 11.2 & Public \\  & [PERSON] et al. [35] & 40.8 & 74.4 & & 13.7 & 38.3 & 15,143 & 91,792 & 1051 & 2210 & 6.5 & Private \\  & [PERSON] et al. [45] & 45.4 & 74.4 & & 18.1 & 38.7 & 13,407 & 85,547 & 600 & 930 & & Public \\  & [PERSON] et al. [50] & 74.4 & & 73.7 & 46.1 & 15.2 & & & & & Private \\  & [PERSON] et al. [51] & 62.8 & 79.4 & 63.1 & 34.4 & 17.2 & 12,463 & 54,648 & 701 & 983 & & Private \\  & [PERSON] et al. [52] & 50.4 & & 47.5 & & 18,370 & 69,800 & 1826 & & & Private \\  & [PERSON] et al. [53] & 75.6 & **80.9** & **75.8** & 43.1 & 21.5 & 9786 & **34,214** & 448 & & **61.7** & Private \\  & [PERSON] et al. [60] & 63.8 & & 70.6 & 30.3 & 30.6 & 7412 & 57,975 & 629 & & 11.2 & 54.7 & Private \\  & [PERSON] et al. [62] & **76.7** & & 73.1 & **49.1** & **10.7** & 10,689 & 30,428 & 1420 & & Private \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The performance using the MOT15, 16, 17, and 20 datasets. Similarly, the performance of the KITTI dataset is shown in Table 6. The dataset is divided into multiple sequences: car, pedestrian, and cyclist. The methods are either evaluated on all of them combined or individually. As it can be observed, the pedestrian data are the most difficult to process with a good performance. Only methods evaluated on all sequences are used for comparison, and accordingly, the values corresponding to the best performance according to the metric are in bold.