# An Effectively Finite-Tailed Updating for Multiple Object Tracking in Crowd Scenes
## 4 Experiment  
### Experimental Setting  
Datasets: Similar to some earlier work [21; 27], we use ETH [29], CityPerson [30], CalTech [31], MOT17 [32], CUHK-SYSU [33], and PRW [34] as our training datasets. We only use the ETH and CityPerson datasets to train the detection branches because they only have box annotations. There are both box annotations and identity annotations in the other datasets. We use them to train the Detection and Re-ID branches at the same time. Because some data in ETH also appear in the test set of MOT16, we removed these data from the ETH training set to be fair. Metrics: The performance of our algorithm is evaluated based on the datasets of the MOT Challenge Benchmark. We extensively evaluate a variety of factors of our approach on the testing sets of three benchmarks: MOT16, MOT17, and MOT20. We adopt the CLEAR [35] MOT metrics, MOTA, IDF1, HOTA, ML, and MT, for our experimentation and comparison against the state-of-the-art methods. Multi-Object Tracking Accuracy (MOTA) denotes standard deviation across all sequences. This measure combines three error sources: false positives, false negatives, and identity switches. IDF1 denotes the ratio of correctly identified detections over the average number of ground-truth and computed detections. The formula for MOTA and IDF1 are shown in Formulas (9) and (10). The \(GT_{t}\), \(FP_{t}\), \(FN_{t}\), and \(IDSW_{t}\) represent the number of ground-truth bounding boxes, false positives, false negatives, and identity switches at time t, respectively. The IDTP, IDFP, and IDFN are  
Figure 3: The architecture picture of our approach, \(T_{0}\) represents the initial frame, \(T_{n}\) represents the current frame, and \(\lambda\) is a hyperparameter for calculating the cost matrix. Bounding boxes and identities are marked in the frames. Bounding boxes with different colors represent different identities. the total number of true positive, false positive, and false negative object identifications, respectively. The Higher-Order Tracking Accuracy (HOTA) is a newly defined indicator. The previous indicator overemphasized the importance of detection or correlation. To solve this problem, we use the new MOT evaluation index, HOTA, which balances the effects of performing accurate detection, correlation, and positioning into a single unified index for comparing trackers. MT denotes mostly tracked objects, it represents the ratio of ground-truth trajectories that are covered by a track hypothesis for at least 80% of their respective life spans. ML denotes mostly lost objects. It represents the ratio of ground-truth trajectories that are covered by a track hypothesis for at most 20% of their respective life spans. \[MOTA=1-\frac{\sum_{t}(FP_{t}+FN_{t}+IDSW_{t})}{\sum_{t}GT_{t}} \tag{9}\]  
\[IDF1=\frac{2 IDTP}{2 IDTP+IDFP+IDFN} \tag{10}\]  
It can be seen from the above formula that the MOTA indicator focuses on the IDSW. The greater the number of IDSW, the lower the score of MOTA. However, while it simply considers the number of IDSW, it cannot determine whether the ID has changed back to the correct ID. The IDF1 indicator focuses on the identification of the ID. The higher the IDF1 score, the higher the proportion of the correct ID number. Therefore, in this paper, we pay more attention to the IDF1 indicator than the MOTA indicator  
Implementation details: Our encoder-decoder is a fully-convolutional DLA-34 variant. The model parameters pre-trained on the COCO dataset [36] are used to initialize our model. We train our model with an Adam optimizer [37] for 30 epochs with a starting learning rate of \(1e^{-4}\). The batch size is set to be 12. The input frame is resized to \(1088\times 608\) and the feature map resolution is \(272\times 152\). The confidence threshold for tracking is set to 0.4. The hyperparameter of the control motion model in the cost matrix is set to 0.9815. For the first association between objects, we integrate the motion model to find a match and set the threshold to 0.4.