# Multiple Object Tracking in Robotic Applications: Trends and Challenges
## 4 Evaluation and Discussion
The rest are listed for observation and analysis. The approach introduced in Ref. [46] showed superiority. Although it did not perform well on the MOT15 dataset, it showed a competing performance on the MOT17 dataset. The authors in Ref. [102] evaluated the proposed technique on each sequence individually. They have superior performance on all of them. The performance on the UA_DETRAC dataset is shown in Table 7. Although the UA_DETRAC dataset is of a static background type and does not include the challenge of dynamic background, the approach introduced in Ref. [47] performed better on the KITTI dataset. This variation in the performance of the MOT techniques on multiple datasets may indicate that the MOT techniques are data driven and difficult to generalize. Similar to 2D Tracking, the deep learning approach is utilized for visual feature extraction in 3D. For processing point cloud data, PointNet is the most popular method. The authors in Refs. [85; 89] did not rely on deep learning for techniques for the processing of point cloud data and performed poorly in terms of accuracy on the KITTI dataset shown in Table 6. The approach with the highest accuracy in Table 3 introduced in Ref. [86] creates multiple solutions for data fusion problems. The features extracted from LIDAR and camera sensors are fused by concatenation, addition, or weighted addition and then passed into a custom-designed network to calculate the correlation between the features and output the linked detections. The approaches that depend on deep learning for data fusion, Refs. [85; 87; 90], have a low MOTA, although [90] has high accuracy on the car set. The localization-based Tracking introduced in Ref. [103] has the best accuracy on the UA_DETRAC dataset. Although the DMM-Net method in Ref. [104] has significantly lower accuracy, it showed superiority in identity switches and fragmentation. \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. [47] & 77.1 & 78.8 & 51.4 & 8.9 & 760 & 6998 & 123 & & Car \\ [PERSON] et al. [85] & 75.7 & 78.5 & 58.0 & 5.1 & & & & & All \\ [PERSON] et al. [86] & 84.8 & 85.2 & 73.2 & 2.77 & 711 & 4243 & 284 & 753 & All \\ [PERSON] et al. [70] & 80.4 & 81.3 & 62.8 & 6.2 & & & 121 & 613 & Car \\ [PERSON] et al. [87] & 76.15 & 83.42 & 60.0 & 8.31 & & & 296 & 868 & All \\ [PERSON] et al. [71] & 84.5 & 85.6 & 73.4 & 2.8 & **705** & **4242** & & & All \\ [PERSON] et al. [88] & 82.2 & 84.1 & 64.9 & 6 & & & 142 & 416 & Car \\ [PERSON] et al. [36] & 71.3 & 81.8 & 48.3 & 5.9 & & & & & All \\ [PERSON] et al. [102] & 86.2 & 78.43 & & & & & 0 & 15 & Car \\ [PERSON] et al. [102] & 70.9 & & & & & & & & Pedestrian \\ [PERSON] et al. [102] & 84.9 & & & & & & & & Cyclist \\ [PERSON] et al. [105] & 84.8 & & & & 681 & 4260 & 275 & & Car \\ [PERSON] et al. [106] & 68.2 & 76.6 & 60.6 & 12.3 & & & 111 & **725** & All \\ [PERSON] et al. [89] & 78.10 & 79.3 & 70.3 & 9.1 & & & 21 & 111 & Car \\ [PERSON] et al. [89] & 46.1 & 67.6 & 30.3 & 38.7 & & & 57 & 500 & Pedestrian \\ [PERSON] et al. [89] & 70.9 & 77.6 & 71.4 & 14.3 & & & 3 & 24 & Cyclist \\ [PERSON] et al. [90] & 85.7 & 85.5 & & & & & 98 & & Car \\ [PERSON] et al. [90] & 46.0 & 72.6 & & & & 395 & & Pedestrian \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The performance using the KITTI dataset. We have marked the highest scores in bold for methods evaluated on all categories. Navigation and self-driving applications in robotics depend on the online feature. The system must be able to react in real time. Although most of the techniques discussed can process video sequences online, their FPS is not showing robust performance, according to the Hz metric, to be deployed in an application such as self-driving. The method introduced in Ref. [43] has the highest FPS overall and acceptable accuracy on the MOT16 and MOT17 datasets.