## 4 Experiment
### Performance Evaluation
#### 4.2.2 Stereo Taillight Matching  
We used 2312 image pairs from the raw dataset to evaluate the proposed matchingClassifier. Initially, all these image pairs were divided into two sets. The first set, including 1312 image pairs, was utilized for generating the training set. The second set, containing the remaining 1000 image pairs, was utilized for generating the test set. The procedure for generating each of these sets is described as follows.  
* From each image pair (\(I_{L}\), \(I_{R}\)), after performing the proposed taillight detection method on each image, two lists of taillights \(\mathbf{L}=\{L_{1}\), \(L_{2}\),..., \(L_{m}\}\) and \(\mathbf{R}=\{R_{1}\), \(R_{2}\),..., \(R_{n}\}\) are achieved.
* All combinations of two taillights (\(L_{i}\), \(R_{j}\)), with each belonging to each list, are generated.
* A function matchingFeatureExtraction processes each combination (\(L_{i}\), \(R_{j}\)) to extract its feature vector _matchVec_, while the expected _status_ value is manually set through direct observation.
* A tuple (_matchVec_, _status_) of (\(L_{i}\), \(R_{j}\)) is added to the dataset.
* A procedure keeps running with other combinations and then with other image pairs.  
The above procedure is illustrated in Figure 26 below:  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Correct Detection** & **False Detection** & **Missed Detection** & **Precision (\%)** & **Recall (\%)** & **F-Score (\%)** \\ \hline
2356 & 171 & 104 & 93.23 & 95.77 & 94.49 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Taillight detection results.  
Figure 26: Constructing datasets for training and testing matchingClassifier.  
To evaluate the performance of matchingClassifier, we focused on the number of taillight combinations that were predicted as \"match.\" Initially, we determined the number of correctly matched combinations _Correct Matching_, the number of falsely matched combinations _False Matching_, and the number of missed combinations _Missed Matching_. They respectively demonstrate _True Positive_, _False Positive_, and _False Negative_. Thanks to that, precision, recall, and f-score rates of matchingClassifier were determined using Equations (35)-(37).  
For 5532 combinations of the two taillights that were expected to match each other from the test set, 5336 matched combinations were correctly predicted as \"match\" and 196 unmatched combinations were falsely predicted as \"match\", while 315 matched combinations were missed, i.e., predicted as \"not match\". The results are shown in Table 5:  
The results reveal that matchingClassifier is relatively good for determining which two taillights do not match, but there are also several missed combinations. These missed combinations occurred primarily because the similarity between the two taillights was hardly observed due to the different fields of view from the two cameras. It is a critical problem since we aim to locate as many taillights as possible so that the positions of their corresponding vehicles can be estimated. Therefore, future works should provide some ways to emphasize the similarity of the corresponding taillights in two images for obtaining a better recall rate.