# A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving
## 7 Experiment Analysis  
In order to compare and analyze the current visual multi-object tracking algorithms based on deep learning, this chapter compares the performance of the algorithms through the MOT17 test set. Combined with the following conditions, the design experimental analysis table is shown in Tables 8 and 9. As introduced in Chapter 2, the larger the HOTA, MOTA, and IDF1 values, the better, and the smaller the IDs, FN, and FP, the better.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Method** & **Dataset** & \multicolumn{1}{c}{**Basic Idea**} &
\begin{tabular}{c} **Encoder-Decoder** \\ **Architecture** \\ \end{tabular} \\ \hline \multirow{8}{*}{[87]} & \multirow{8}{*}{TransTrack} & \multirow{8}{*}{MOT17 [25], MOT20 [26]} & 1. & Using the Query–Key mechanism, the features of two adjacent frames & \\  & & & are combined. & \\  & & & 2. & The object query is used to learn to detect & \\  & & & new objects. & \\  & & & 3. & The track query is responsible for & \\  & & & MOT20 [26] & maintaining the trajectory. One of the two & \\  & & & decoders is responsible for detection and & \\  & & & generates a detection box; the other is & \\  & & & responsible for object propagation and & \\  & & & generates a tracking box. & \\  & & & 4. & Two boxes get the result by IoU matching. & \\  & & & 1. & Using the Query–Key mechanism, the object query detects new objects, and the track query is responsible for tracking the object between frames (same as TransTrack) & Single decoder has \\  & & & MOT20 [26] & 2. & In the decoder, the track queries and object queries are spliced and input, and the MLP directly maps the output of the decoder into classes and boxes. & \\  & & & 1. & Siamese network structure, two adjacent frames are used as input. & \\  & & & 2. & One network is used for detection, the other & \\  & & & network is used for tracking, and & \\  & & & 3. & Multi-scale features are generated for & \\  & & & fusion, respectively, entering three branches & \\  & & & to perform three tasks: predicting the center & \\  & & & heat map, predicting the size of the box, & \\  & & & and predicting the displacement. & \\  & & & 1. & The spatial relationship between objects is & \\  & & & represented by a sparse weighted graph. & \\  & & & 2. & The set of graphs from the past few frames & \\  & & & enters the Encoder to learn the temporal & \\  & & & spatial relationship; the graph of the current & \\  & & & frame enters the Decoder to learn the & \\  & & & spatial relationship, and finally generates & \\  & & & an allocation matrix for matching. & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Algorithm analysis and comparison based on transformer-based framework.  
1. Among the above-mentioned algorithms, the most commonly used is the MOT17 test set. Therefore, in order to maintain fairness, we use the MOT17 data set test indicators to analyze the algorithm.
2. Due to the differences between public detection-based and private detection-based algorithms, the algorithm performance is discussed separately.
3. To highlight the difference between the three tracking algorithm frameworks, we still order these algorithms according to the three frameworks of TBD, JDT, and transformer-based, separated by line segments in the table.