# Cross-Modality Interaction-Based Traffic Accident Classification
## 4 Experiment
### Experimental Setup  
For the first time, the video highlight detection models [12; 13; 14] are applied to the field of traffic accident detection, and their usefulness in detecting accidents is demonstrated. The mAP (mean average precision) for moment retrieval is calculated using 10 predictions and the ground truth, while the mAP for highlight detection is computed with a single prediction and the ground truth. In order to evaluate the performance of highlight accident segment classification according to each model's feature extraction method, an accuracy metric for moment retrieval is added. Accuracy is defined as follows 1.  
\[Accuracy=TP+TN/(TP+TN+FP+FN) \tag{1}\]  
In this context, \(TP\) is the number of accurately predicted highlighted segments, \(FP\) is the number of incorrectly predicted highlighted segments, \(TN\) is the number of correctly predicted non-highlighted segments, and \(FN\) is the number of incorrectly predicted non-highlighted segments. The prediction and the ground truth are classified into video segment clips of two-second intervals, with one indicating a highlight and zero indicating a non-highlight. Accuracy assesses the degree to which these two-second video clips are correctly predicted.  
For the experiments, a GPU: GeForce RTX 3090, CPU: Intel(R) Core(TM) i9-10900K @ 3.70 GHz, running on Ubuntu 22.04, with CUDA version 1.15.0 was utilized. For Moment-DETR, UMT, and QD-DETR, the default settings were used for the main parameters, and the training was conducted for a maximum of 100 epochs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Query** & \multicolumn{6}{c}{**“Motorcycle Hits Motorcycle.”**} \\ \hline \multirow{2}{*}{**Metric**} & \multicolumn{3}{c}{**Moment-DETR**} & \multicolumn{3}{c}{**QD-DETR**} \\  & **CS** & **CR** & **CM** & **CS** & **CR** & **CM** \\ \hline [EMAIL_ADDRESS] & 82.86\% & 82.86\% & 87.14\% & 80.00\% & 77.14\% & 77.14\% \\ MR-Accuracy & 65.71\% & 65.71\% & 74.29\% & 60.00\% & 54.29\% & 54.29\% \\ HL-Min-Fair-mAP & 36.67\% & 36.67\% & 44.29\% & 40.00\% & 46.19\% & 47.62\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparing Moment-DETR and QD-DETR using video and text data (query: “Motorcycle hits motorcycle”, DAD).  
Figure 4: Visualizing the performances of Moment-DETR and QD-DETR (DAD). (a) [EMAIL_ADDRESS], (b) MR-Accuracy; (c) HL-Min-Fair-mAP.  
Figure 5: Visualizing the performances of Moment-DETR and QD-DETR in terms of evaluation metrics, [EMAIL_ADDRESS], MR-Accuracy, and HL-Min-Fair-mAP (DAD). (a) Query: “Car hits car”; (b) query: “Motorcycle hits motorcycle”.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metric** & \multicolumn{3}{c}{**Moment-DETR**} & \multicolumn{3}{c}{**QD-DETR**} \\  & **CS** & **CR** & **CM** & **CS** & **CR** & **CM** \\ \hline [EMAIL_ADDRESS] & 78.75\% & 78.08\% & 78.00\% & 78.17\% & 77.50\% & 78.25\% \\ MR-Accuracy & 57.50\% & 56.50\% & 56.00\% & 56.50\% & 55.00\% & 56.50\% \\ HL-Min-Fair-mAP & 39.42\% & 37.08\% & 44.92\% & 40.00\% & 46.92\% & 49.08\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparing Moment-DETR and QD-DETR with video and text data (DAD).