# A Robust Multi-Camera Vehicle Tracking Algorithm in Highway Scenarios Using Deep Learning
## 3 Methods
### DLA-Efficient
Multi-target tracking precision reflects the tracking algorithm's effectiveness in accurately identifying targets and maintaining their trajectories, serving as a crucial metric for evaluation. A higher MOTA value indicates better efficiency of the tracking algorithm. The formula is as follows:

\[\text{MOTA}=1-\frac{\sum_{t}\left(\text{FN}_{t}+\text{FP}_{t}+\text{IDSW}_{t} \right)}{\sum_{t}\text{GT}_{t}} \tag{8}\]

where the count of undetected detections (false negatives) of FN\({}_{t}\) at time \(t\), the count of erroneous identifications (false positives) for FP\({}_{t}\) at time \(t\), the number of IDs (ID switches) of IDSW\({}_{t}\) at time \(t\), and the actual number of targets of GT\({}_{t}\) at time \(t\) (ground truth). IDs represent the total count of ID switches throughout the video circuit; MT denotes the proportion of tracks adhering to GT and achieving a minimum 80% match. \[\text{MT}=\frac{\text{Number of mostly tracked targets}}{\text{Total number of targets}} \tag{9}\]

Mostly lost (ML) represents the majority of lost tracking cases, defined as the proportion of trajectories that have a ground truth (GT) matching success rate of less than 20% among all the tracking targets. \[\text{ML}=\frac{\text{Number of mostly lost targets}}{\text{Total number of targets}} \tag{10}\]

The IDF1 (Identifier F score) signifies the ratio of precisely ascertained tests compared with the actual count and the calculated average values of these tests. \[\text{IDF1}=2\times\frac{\text{IDTP}}{2\times\text{IDTP}+\text{IDFP}+\text{ IDFN}} \tag{11}\]

where IDTP represents the number of correctly identified targets, IDFP represents the number of falsely identified targets, and IDFN represents the number of missed targets. IDF1 measures the combined performance of the precision and recall of the correctly identified target IDs, with higher values indicating better performance. The multi-target tracking precision directly mirrors how effective the tracking algorithm is in identifying targets and maintaining their paths, serving as the critical metric for evaluation. With MOTA's proximity, the efficiency of the tracking algorithm is enhanced. The IDs are calculated by adding up the number of IDs throughout the video track, while MT represents the proportion of tracks adhering to the GT and achieving a minimum 80% match. Mostly lost tracks (ML) represent the majority of lost tracks, the proportion of tracks that satisfy all the GT tracking targets, and the match success rates of less than 20%; \(IDF1\) represents the proportion of accurately pinpointed tests to the actual count and their computed averages. ### Implementation Details

Our implementation involved Ubuntu 20.04 and three RTX3060 GPU cards. A virtual setting was initiated via Anaconda 3, and the deep learning environments Python 3.7 and Pytorch 1.6 were set up, training and evaluating the FairMOT-MCVT car tracking system utilizing the UA-DETRAC. Throughout the training, these parameters were established: 60 epochs, all six batch numbers, starting with a \(1\times 10^{-4}\) learning rate, which was diminished to \(1/10\) of the initial value in the thirtieth training phase--the training involved using the Adam optimizer, maintaining a momentum parameter of 0.8. Concurrently, standard strategies for enriching data encompass ambiguity, translation, and shearing. ### Ablation Studies

Firstly, an ablation experiment using a DLA-efficient network was performed to illustrate various enhancements to the model. This experiment analyzed the variations in inference speed and precision across 6000 images for each refined COCO [43] test collection. Alterations in training deficits are depicted in Figure 7. The alteration in the total loss curve illustrates that the optimized feature extraction network achieves steadier training processes, quicker convergence speeds, and improved training outcomes.