# Multiple Object Tracking in Robotic Applications: Trends and Challenges
## 6 Future Work  
The final objective of the research done on deploying MOT algorithms in autonomous robots is to have a reliable system that contributes to reducing accidents and facilitating tasks that might be difficult for humans to carry out. One aspect that we found the current research lacks is the generation of a new benchmark dataset that includes data collected by the standard sensors employed by the current industry. Sensors such as ultrasonic and LiDAR are essential in today's autonomous robot manufacturing, and it is necessary to use the same tools to make the research on MOT up-to-date. Moreover, using deep  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Tracker** & **MOTA \(\uparrow\)** & **MOTP \(\uparrow\)** & **MT \(\uparrow\)** & **ML \(\downarrow\)** & **FP \(\downarrow\)** & **FN \(\downarrow\)** & **IDS \(\downarrow\)** & **Frag \(\downarrow\)** & **Type** \\ \hline [PERSON] et al. [46] & **89.4** & 85.1 & **82.3** & **2.3** & & **116** & 744 & All \\ [PERSON] et al. [33] & 85.0 & **85.5** & 74.3 & 2.8 & & **301** & & All \\ [PERSON] et al. [57] & 91.2 & & 86.5 & 2.3 & & & & Car \\ [PERSON] et al. [57] & 67.7 & & 49.1 & 14.5 & & & & Pedestrian \\ [PERSON] et al. [58] & 90.3 & & & & & 250 & 280 & Car \\ [PERSON] et al. [58] & 65.1 & & & & & 204 & 609 & Pedestrian \\ [PERSON] et al. [107] & 90.4 & 85.0 & 84.6 & 7.38 & 2322 & 962 & & & Car \\ [PERSON] et al. [107] & 52.2 & 64.5 & 35.4 & 25.4 & 1112 & 2560 & & & Pedestrian \\ [PERSON] et al. [108] & 86.9 & 85.7 & 83.1 & 2.9 & & 271 & 254 & Car \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Dataset** & **Tracker** & **PR-MOTA \(\uparrow\)** & **PR-MOTP \(\uparrow\)** & **PR-MT \(\downarrow\)** & **PR-FR \(\downarrow\)** & **PR-FN \(\downarrow\)** & **PR-IDS \(\downarrow\)** & **PR-FRAG \(\downarrow\)** \\ \hline  & [PERSON] et al. [68] & 30.7 & 37 & 32 & 22.6 & 18,046 & 179,191 & 363 & 1123 \\  & [PERSON] et al. [72] & 14.5 & 36 & 14 & 18.1 & 38,597 & 174,043 & 799 & 1607 \\  & [PERSON] et al. [69] & 30.3 & 36.3 & 30.2 & 21.0 & 20,263 & 179,317 & 389 & 1260 \\  & [PERSON] et al. [42] & 20.2 & 26.3 & 14.5 & 18.1 & 9747 & **135,978** & & \\ UA\_DETRAC & [PERSON] et al. [47] & 19.8 & 36.7 & 17.1 & 18.2 & 14,989 & 164,433 & 617 & \\  & [PERSON] et al. [103] & **46.4** & **69.5** & **41.1** & 16.3 & & & \\  & [PERSON] et al. [104] & 12.2 & & 10.8 & 14.9 & 36,355 & 192,289 & **228** & **674** \\  & [PERSON] et al. [109] & 31.2 & 50.9 & 28.1 & 18.5 & **6036** & 170,700 & 252 & \\  & [PERSON] et al. [110] & 22.5 & 35.2 & 15.5 & **10.1** & & & 1563 & 3186 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The performance using the UA_DETRAC dataset.  
learning algorithms for detection and tracking would face a massive problem due to the risk of meeting a variation that was not included in the training set. Thus, deep learning should be trained on segmenting the road regions and hence, be trained on any area before deployment. This is one of the approaches that can be researched to tackle the problem of dealing with new objects. The current research looks at appearance and motion models as necessary in MOT. They are further going into learning the behavior of objects in the scenes and the interactivity between those objects. For instance, two objects moving towards each other would lead to one of them getting covered, and a track would be lost. As the MOT system's complexity increases, it becomes more challenging to work in real-time. The research on embedded processors that can be utilized in autonomous robots will significantly contribute to increasing the accuracy while maintaining the online feature.