# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
## 3 Results
### Datasets and Metrics
#### 3.1.2 Evaluation Metrics  
We used the following metrics to evaluate the performance of the tracking algorithm: Multiple Object Tracking Accuracy (MOTA) [34], Identification F1 Score (IDFI) [35], Higher Order Tracking Accuracy (HOTA) [36], Detection Accuracy (DetA) [36], and association accuracy (AssA) [36]. MOTA measures tracking accuracy by evaluating three types of tracking errors: False Negatives (FN) and False Positives (FP) in object detection, and Identification Switches (IDSW) in association errors. MOTA focuses more on detection performance, with values ranging from less than or equal to 1, where 1 indicates the best case. A higher score signifies better tracking accuracy. The calculation formula for MOTA is shown in Equation (20), where \(t\) represents the frame number in the video sequence, \(\text{GT}_{t}\) represents the number of ground truth instances in the \(t\)th frame, \(\text{FN}_{t}\) represents the number of False Negatives in the \(t\)th frame, \(\text{FP}_{t}\) represents the number of False Positives in the \(t\)th frame, and \(\text{IDSW}_{t}\) represents the number of identity switches in the \(t\)th frame. \[\text{MOTA}=1-\frac{\frac{\sum\limits_{t}\left(\text{FN}_{t}+\text{FP}_{t}+ \text{IDSW}_{t}\right)}{\sum\limits_{t}\text{GT}_{t}}}{\text{IDF1}\text{ assesses tracking continuity and re-identification accuracy by calculating the ratio of correctly identified detections to the average of the ground truth detections and the computed detections. IDF1 emphasizes association performance, with values ranging from 0 to 1, where 1 represents the best case. A higher score reflects better tracking association. The calculation formula for IDF1 is shown in Equation (21), where IDTP (True Positive ID) represents the correctly matched object identities, i.e., the cases where the ground truth objects are correctly matched with the tracking results. IDFP (False Positive ID) represents the incorrectly matched object identities, i.e., the cases where the ground truth objects are incorrectly matched with the tracking results. IDFN (False Negative ID) represents the ground truth object identities that are not matched, i.e., the cases where ground truth objects are not matched with any tracking results.  
\[\text{IDF1}=\frac{\text{2 IDTP}}{\text{2 IDTP}+\text{IDFP}+\text{IDFN}} \tag{21}\]  
DetA is a metric used to measure the accuracy of object localization in detection. It primarily evaluates the overlap between detected bounding boxes and ground truth boxes by calculating their Intersection over Union (IoU). The score ranges from 0 to 1, with 1 representing the best case. The calculation formula for DetA is shown in Equation (22), where TP represents a True Positive, FN represents a False Negative, and FP represents a False Positive. \[\text{DetA}=\frac{\text{TP}}{\text{TP}+\text{FN}+\text{FP}} \tag{22}\]  
AssA evaluates the accuracy of data association in tracking algorithms, reflecting the algorithm's ability to avoid IDSW and association errors. Its values range from 0 to 1, with 1 indicating the best performance. The calculation formula for AssA is shown in Equation (23), where TPA represents the number of True Positive matches between two trajectories, FN represents the number of False Negative matches between two trajectories, and FP represents the number of False Positive matches between two trajectories. \[\text{AssA}= \frac{1}{\text{TP}}\sum_{c\in\text{TP}}\frac{\text{TPA}(c)}{\text{ TPA}(c)+\text{FNA}(c)+\text{FPA}(c)} \tag{23}\]  
HOTA explicitly balances the effects of detection, association, and localization into a unified metric. HOTA scores better reflect human visual evaluation of tracking performance. Its values range from 0 to 1, with 1 indicating the best performance. A higher score represents better overall tracking results. The calculation formula for HOTA is shown in Equation (24), where \(\alpha\) represents the localization threshold. \[\text{HOTA}=\int\limits_{0<\alpha\leq 1}\text{HOTA}_{\alpha}\approx\frac{1}{19} \sum\limits_{