## 4 Experiments
### Point Tracking  
Diverging from conventional object trackers, which prioritize enhancing the alignment between tracking outcomes and designated labels [29], the focus of this section revolves around the acquisition of a continuous sequence of edge feature points. This sequence holds significance as it is subsequently utilized to optimize parameters in a seamless manner. To achieve accurate tracking, the fundamental requirement is that the current pixel displacement of vehicles falls within the confines of the generated tracking search area. In the context of the prior video dataset, Formula (10) is employed. This leads to determining the upper and lower coefficients for the longitudinal displacement model, equating to 1.13 and 1.09, respectively. For the lateral model, the coefficients are 1.1 and 0.98.  
Illustrated in Figure 10, the true longitudinal displacement is depicted by the blue curve, while the true transverse displacement is represented by the pink curve. Notably, both of these curves are situated within the boundaries delineated by their upper and lower\(-\)fitting counterparts. This configuration is instrumental in guaranteeing that the target vehicle consistently resides within the confines of the local search area, thereby ensuring effective tracking. Furthermore, the pixel disparity between the actual and fitted displacements remains minimal, a crucial attribute that leads to a reduction in matching computations.  
Figure 10: Edge points’ displacement between adjacent frames.  
Figure 9: Point location results of the two measure indexes: (**a**) point location results of the vertical measure index; (**b**) point location results of the horizontal measure index. The pink line is the Fraction of test vehicles.  
In this section, the missing rate (MR), misjudgment rate (FPR), and mismatching rate (MMR) from the evaluation criteria CLEAR [30] are used to quantify the multi\(-\)vehicle tracking performance. MR indicates the ratio between the tracking loss number and the total vehicle number; MMR represents the ratio between the number of vehicles that match incorrectly and the total; and FPR represents the percentage of candidate vehicles that cannot match the tracking target and the total.  
To substantiate the efficacy of the proposed tracking method, which relies on the dynamically generated search area as detailed in this paper, a series of experiments were conducted. These experiments were conducted in conjunction with two alternative tracking approaches: individual Kalman tracking and feature extraction operator\(-\)based tracking [31]. The evaluation was performed within a video dataset encompassing diverse lighting conditions. The cumulative results of these experiments are presented in Table 1. The independent Kalman tracking method demonstrates a consistent ability to effectively track vehicles with relatively gradual speed changes within a confined scope. However, when traffic congestion intensifies, the tracking effectiveness diminishes steadily due to the challenges presented by erroneous matches. On the other hand, a tracker based on feature extraction and reconstruction fails to utilize the existing detection information effectively. Features extracted manually not only have poor representational ability but also add extra computation. The MR, FPR, and MMR indexes of the tracking method in this paper are all smaller than those of the other three methods. Edge point matching optimization based on local tracking search areas eliminates the need for complex calculations, unlike the previous global matching approach. Moreover, the correlation calculation of motion and appearance similarity increases target tracking stability. Only when the convolutional network's detection result is wrong will it have certain missing and mismatched vehicles. When \(\alpha=0.56\), a stable vehicle tracker is obtained with 96.2% average accuracy.