## 4 Experiments and Results
### Datasets and Evaluation Metrics  
We selected diverse MOT datasets--MOT15, MOT16, MOT17, MOT20, DanceTrack, and WildTrack--to evaluate the proposed framework comprehensively. These datasets span a wide range of real-world scenarios, including urban environments, indoor and outdoor scenes, and varying levels of crowd density, as provided in Table 1 in detail. MOT15 includes indoor and outdoor environments with moderate difficulty due to occlusions and varying densities. MOT16 and MOT17 share content but differ in annotation styles, allowing for an assessment of the impact of annotation on performance in crowded urban settings. MOT20 features extremely crowded scenes with prolonged occlusions, ideal for dense urban applications. DanceTrack focuses on dynamic, fast-paced indoor movements, challenging the model with rapid motion and complex interactions. WildTrack emphasizes multi-camera setups and varying angles, posing synchronization challenges. These datasets were selected for a comprehensive evaluation, covering scenarios from crowded urban streets to controlled environments. We converted annotations to YOLOv8 format, concentrating on the person class for consistency across datasets. Each image corresponds to a label file formatted with one row per object ([_class_, _xcenter_, _ycenter_, _width_, _height_]), normalized between 0 and 1 to meet YOLOv8's input requirements, facilitating efficient fine-tuning. To evaluate tracking per formance, we used a comprehensive set of metrics, including accuracy, precision, identity preservation, and robustness. The CLEAR MOT metrics, focusing on detection quality, track continuity, and identity preservation, are standard for assessing MOT performance. We employed the followed metrics to assess the performance of MOT algorithms:  
* **MOTA (Multiple-Object Tracking Accuracy):** MOTA is a widely used metric that accounts for three types of errors: FP, FN, and IDSw. It is calculated as given in Equation (20): \[MOTA=1-\frac{FN+FP+IDSw}{GT}\] (20) where \(GT\) is the total number of ground truth (GT) objects. MOTA provides a general overview of tracking performance by counting how many errors are made by the tracker in total, with higher values indicating better performance. * **MOTP (Multiple-Object Tracking Precision):** MOTP measures the alignment precision between the GT and the tracker's output. It is the average distance between the predicted bboxes and the GT across all matches, where the lower values indicate higher precision, as given in Equation (21): \[MOTP=\frac{\sum_{l}\sum_{i}d_{i,t}}{\sum_{t}c_{t}}\] (21)
* **IDSw:** IDSw occurs when a tracked object is mistakenly given a different ID than it had in previous frames, and counts the number of times an object's predicted identity changes in the tracker output. Fewer IDSws indicate better tracking performance in maintaining object identities over time. * **FM (Fragmentation):** FM counts the number of times a track is interrupted by measuring how often an object's trajectory is interrupted (i.e., when the tracker loses track of the object and then ReIDs it), with a correct identification being split into two or more separate tracks. Lower fragmentation indicates better continuity in tracking. * **MT (Mostly Tracked):** MT refers to the number of ground truth trajectories that are successfully tracked for at least 80% of their length. A higher MT value indicates better tracking performance. * **PT (Partially Tracked):** PT refers to the number of ground truth trajectories that are tracked for 20% to 80% of their length. PT provides additional insight into tracking performance for partially visible objects.