# Single-Task Joint Learning Model for an Online Multi-Object Tracking Framework
## 3 Experiments
### Evaluation Multiple Object Tracking Performance  
In this section, a thorough assessment of the multi-object pedestrian tracking approach is provided. The experiments utilized video footage from various datasets as testing sources, including S2L1 and S2L2 from PETS2009 [46] as well as TUD-Crossing [47]. The experimental videos are listed in Table 2.  
Multiple-object tracking accuracy (MOTA), precision (MOTP), false negatives (FN), false positives (FP), ID switches, mostly tracked (MT), and mostly lost (ML) are standard performance metrics. FP indicates incorrect detections, while FN refers to missed objects. In tracking by detection, FN and FP are mainly influenced by the detector, and there is often a trade-off; increasing sensitivity reduces FN but raises FP, and vice versa. ID switches are crucial in tracking, as the goal is to maintain consistent object IDs. However, challenges like occlusions and similar appearances often cause ID switches. MOTA and MOTP measure overall tracking performance, while MT and ML assess trajectory completeness. A trajectory was considered MT if tracked for over 80% of its duration, and ML if tracked for less than 20%, without accounting for ID consistency. The data association strategies of the tracking approach were evaluated using two distance metrics derived from Section 2.3: the position distance \(d^{(1)}\) and feature distance \(d^{(2)}\). Two approaches were assessed; the first utilized only the position distance for linking and the latter amalgamated both distances. The latter ensured that the detection boxes closely matched the position and features before associating with the tracker. The experiment utilized PETS09-S2L1 and TUD-Crossing videos for testing and evaluating the tracking performance through four key metrics: FP, FN, IDs, and MOTA. As shown in Figure 8, the results demonstrated the impact of combining both position and feature distances in multi-object tracking. Using only the position distance \(d^{(1)}\) for linking \begin{table}
\begin{tabular}{c c c c c} \hline  & **FPS** & **Resolution** & **Frame** & **Tracks** \\ \hline PETS09-S2L1 & 7 & \(768\times 576\) & 795 & 19 \\ PETS09-S2L2 & 7 & \(768\times 576\) & 436 & 42 \\ TUD-Crossing & 25 & \(640\times 480\) & 201 & 13 \\ \hline \end{tabular}
\end{table} 
Table 2: Tracking experiment datasets. Figure 7: Comparison of our model with state-of-the-art pedestrian re-identification Techniques: (a) CUHK03 dataset, (b) Market-1501 dataset, (c) Duke-MTMC-reID dataset. resulted in suboptimal tracking, with a higher FP and IDs, a slight FN increase, and a lower MOTA. Conversely, employing both distance metrics, \(d^{(1)}\) and \(d^{(2)}\), significantly reduced the FP and IDs, leading to a higher MOTA. The figure clearly illustrates how the combination method outperformed the use of position distance alone, leading to more robust tracking results across various test scenarios. These findings emphasize that considering only position distance leads to more linking errors, while incorporating feature distance effectively mitigates these issues, improving tracking accuracy in diverse environments. This section outlines the comparison of four tracker initialization methods to improve tracking results, as follows:  
Continual determination (CD): A trial tracker is established for potential new objects, requiring successful tracking for at least three frames before official initialization;  
Detection confidence (DC): Detection boxes with confidence scores above a threshold are directly initialized;  
Tracklet confidence (TC): Similar to CD, a trial tracker is created for new objects, but initialization occurs only when tracklet confidence exceeds a threshold;  
Detection + tracklet confidence (DC + TC): Trial trackers are first created using detection confidence then initialized once both detection and tracklet confidence surpass set thresholds. This experiment involved testing PETS09-S2L1 and TUD-Crossing videos with evaluations based on FP, FN, IDs, and MOTA (Figure 9).