# A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving
## 7 Experiment Analysis
### Overall Analysis  
The index scores of the multi-object tracking algorithms of the three frameworks tested in the MOT17 data set are listed in Tables 6 and 7. The overall analysis is as follows according to the test results:  
1. The assessment indicators MOTA and IDF1, based on private detectors, are improved by 9.1 and 4.1, respectively, from the test indicators, based on public detectors, such as the transformer-based TrackFormer algorithm, when compared with public detectors. Therefore, the effectiveness of the front-end detector has a significant impact on multi-object tracking. Enhancing the detector's performance is essential for enhancing multi-object tracking's performance.
2. From the test index MOTA, the TBD framework, JDT framework, and transformer-based framework gradually improve the accuracy of multi-object tracking, the transformer-based algorithm especially constantly refreshes the current optimal performance on the dataset, showing the potential of transformer tracking.
3. The performance of IDs is a crucial indicator of the evaluation algorithm in practical applications, and the handling of the occlusion problem is a problem that must be taken seriously in traffic scenarios, as can be seen from Equation (1), which shows that  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Framework** & **Ref.** & **Method** & **Year** & **HOTA** & **MOTA** & **IDF1** & **IDs** & **FPs** \\ \hline \multirow{4}{*}{TBD} & [50] & SORT & 2016 & 34.0 & 43.1 & 39.8 & 4842 & 143.3 \\  & [65] & MHT\_bLSTM & 2018 & 41.0 & 47.5 & 51.9 & 2069 & 1.9 \\  & [57] & eHAF17 & 2018 & — & 51.8 & 54.7 & 1843 & 0.7 \\  & [69] & OCSORT & 2021 & 52.4 & 58.2 & 65.1 & 784 & 28.6 \\  & [68] & BLSTM\_MTP & 2021 & 41.3 & 51.5 & 54.9 & 2566 & 20.1 \\ \hline \multirow{4}{*}{JDT} & [70] & Tracktor++ & 2019 & 42.1 & 53.5 & 52.3 & 2072 & 1.5 \\  & [75] & JDE & 2020 & — & 62.1 & 56.9 & 1608 & 30.3 \\  & [73] & CenterTrack & 2020 & 52.2 & 67.8 & 64.7 & 3039 & 3.8 \\  & [72] & MIF & 2020 & — & 60.1 & 56.4 & 2556 & 7.2 \\ \hline \multirow{4}{*}{Transformer-based} & [87] & TransTrack & 2021 & — & 74.5 & 63.9 & 3663 & — \\  & [88] & TrackFormer & 2021 & — & 65.0 & 63.9 & 3528 & — \\ \cline{1-1}  & [89] & TransCenter & 2021 & 51.4 & 68.2 & 61.4 & 4102 & 1.0 \\ \cline{1-1}  & [90] & TransMOT & 2021 & — & 68.7 & 72.2 & 2346 & 20.1 \\ \hline \hline \end{tabular}
\end{table}
Table 8: In the MOT17 dataset [25], the algorithm evaluation index based on the public detector.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Framework** & **Ref.** & **Method** & **Year.** & **HOTA** & **MOTA** & **IDF1** & **IDs** & **FPs** \\ \hline \multirow{4}{*}{TBD} & [51] & DeepSORT & 2017 & 54.4 & 60.3 & 61.2 & 2821 & 20.8 \\  & [59] & StrongSORT & 2022 & 64.4 & 79.6 & 79.5 & 1194 & 7.1 \\  & [71] & FFT & 2020 & — & 56.5 & 51.0 & 5672 & — \\ JDT & [74] & CTracker & 2020 & — & 66.6 & 57.4 & 5529 & 34.4 \\  & [12] & FairMOT & 2021 & 59.3 & 73.7 & 72.3 & 3303 & 23 \\  & [87] & TransTrack & 2021 & 54.1 & 75.2 & 63.5 & 3603 & 59.2 \\ Transformer-based & [88] & TrackFormer & 2021 & 57.3 & 74.1 & 68.0 & 2829 & 5.7 \\  & [89] & TransCenter & 2021 & 54.5 & 73.2 & 62.2 & 4614 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 9: In the MOT17 dataset [25], the algorithm evaluation index based on the private detector.  
the IDs index has a relatively small impact on MOTA. According to the information provided above, several tracking algorithms are designed with the goal of reducing IDs and significantly enhancing MOTA; yet, relatively speaking, the performance of the most sophisticated tracking algorithms on IDs indicators is still subpar.
4. Considering that the majority of the challenges use the best hardware for algorithm testing at the moment, the FPs index is constrained by the inconsistency of the tested hardware and software platforms and cannot be used as an absolute algorithm speed assessment. As a result, the majority of the multi-object tracking algorithms used today have trouble keeping up with real-time multi-object tracking.