# Highly Accurate Deep Learning Models for Estimating Traffic Characteristics from Video Data
## 4 Results Analysis
### Traffic Volume and Abnormal Traffic Scenarios Detection  
Modified fairness multi-object tracking (mFairMOT) models, along with different specific detection logic, was used to extract traffic operation parameters and emergency events from roadside video recordings including vehicle speed, traffic flow by lane, vehicle types, individual vehicle trajectory, accidents, abnormal parking, pedestrians on the road, unexpected objects, traffic congestion, vehicle fires, etc. Two types of mFairMOT tracking models were trained; one is an mFairMOT-vehicle tracking model, and the other is an mFairMOT-person tracking model. (1) Abnormal parking  
The modified fairness multi-object tracking (mFairMOT) neural network model was trained to assign each vehicle a unique ID. Even if the vehicle is shadowed for a while, the ID remains the same when it appears again once object features are correctly extracted and stays the same for a short period. The system issues an alarm when the same ID is found after one minute on the screen with the prerequisite that there is no 'traffic congestion' and no 'pedestrian on the road'. Figure 6 displays the detection of abnormal parking scenarios. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Distance (Meter)** & **Side View** & **Right Frontal View** \\ \hline \(\leq\)20 & 96\% (daytime)/92\% (nighttime) & 98\% (daytime)/94\% (nighttime) \\ \hline \(\leq\)50 & 91\%(daytime)/84\%(nighttime) & 95\% (daytime)/88\% (nighttime) \\ \hline \(>\)50 & 75\%(daytime)/70\%(nighttime) & 85\% (daytime)/80\% (nighttime) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Speed detection accuracy on different perspective settings. (2) People on the road  
The mFairMOT pedestrian tracking neural network model was trained to assign each person a unique ID when a person is detected. Even if the same person disappears for a while, the ID remains the same when it shows up again. The system issues an alarm when it detects a pedestrian walking on the freeway for five consecutive frames as shown in Figure 7.  
Figure 6: Abnormal parking detection (straight lines denote detection region). Figure 7: Person on freeway detection. (3) Traffic congestion  
The mFariMOT vehicle tracking model calculates the number of vehicles on the screen. If the intersection of union (IOU) (area of overlap/area of union) is larger than 0.5 (can be tuned for specific scenarios), these restrictions are satisfied in the consecutive 10 frames. The system's alarm is triggered and reports traffic congestion status to the respective control centre(s). The reason that the threshold is set to the tuning parameter is to reduce false positive calls for different traffic scenarios. Figure 8 displays the detection of congestion. (4) Vehicle fire  
The mFairMOT network is programmed to detect smoke and fire at the same time for consecutive 3 frames. If either fire or smoke is detected, then no alarm is triggered for fire. Fire and smoke classes have already been pre-trained and stored in the mFairMOT backend database. Figure 9 displays the detection of vehicle fires. In summary, detection accuracy on different perspective settings is displayed in Table 3.