# Highly Accurate Deep Learning Models for Estimating Traffic Characteristics from Video Data
## 5 Discussion  
Automatic crash detection and traffic flow analysis contribute significantly to proactive traffic safety management. Current standards and models for traffic crash prediction focus on sensor-dependent data models and external hardware installations (e.g., radar) [23; 26]. Such systems suffer from the deficiency of imposing extra costs during implementation and maintenance. Moreover, there is a dearth of pre-existing roadway cameras with smart traffic analysis systems, the majority of which rely on manual monitoring of traffic footage. As an alternative to the aforementioned systems with high implementation costs and to make full use of pre-existed roadside cameras, this study developed a real-time system for multi-vehicle motion parameters with a typical roadside surveillance camera. The resulting speed prediction demonstrated reasonable accuracy when compared to the ground truth obtained from the LiDAR. The PID filter was successful in suppressing the noise in the predictions of vehicles close to the camera; however, the suppressing effect was marginal for vehicles over \(\approx\) 50 m from the camera (with an average decrease of 27% in object tracking accuracy). Much of this accuracy fluctuation was incurred by discrete prediction through different frames [40]. As the prediction of the bounding box of an object does not consider the prediction in the previous frames, the bounding box of the same object may be of drastically different shape or ratio through a sequential series of frames. While this disparity in shape only affects the resultant position prediction of the vehicle marginally, the resultant noise in the vehicle's position severely affects the speed prediction in a further range, where the noise-suppressing PID is no longer effective. Estimation accuracy largely varies between day to night because of the lighting conditions. Compared to radar or LiDAR, speed estimation from a computer vision technique is cost-effective and can return more abundant information such as vehicle types, traffic flow, trajectory, and abnormal incidents at lower cost. The modified FairMOT model changes the Resnet 34 backbone network of the original FairMOT model to CSPDarknet53. Compared to Resnet34, CSPDarnet53 has more layers to capture deeper object features and is a light but effective learning neural network that consists of residual blocks. The modified FairMOT model has better detection and re-ID ability in traffic scenarios. The following Table 5 compares model performance of the modified FairMOT with other state-of-the-are tracking models in the tested scenario. The modified FairMOT model outperforms other tracking models in the tested scenario. The mFairMOT model balances detection and tracking processes fairly and improves re-identification branch through integrating CSPDarknet53 and retaining DLA structure in FairMOT to fuse aggregated features. YOLO-v8 has trouble in detecting small objects and provides various model sizes which sacrifice re-ID performance. DeepSORT has advantages in maintaining detection in occluded environments and can differentiate between objects in complex environments. However, DeepSORT is a two-stage model and trains separate detection networks, requiring extensive datasets for high accuracy. SORT is the \begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multirow{2}{*}{**Model**} & **IDF1—The Fraction of Correctly Identified Detections over the Average Number of True Detections** & **Multiple Object Tracking Precision (MOTP)—Measures the Accuracy of Detection Box Localization** & **Multi-Object Tracking Accuracy—MOTA Measures the Overall Accuracy of both the Tracker and Detection** \\ \hline mFairMOT & 96.0\% & 98.5\% & 95.7\% \\ \hline YOLOv8 & 94.5\% & 97.2\% & 94.3\% \\ \hline SORT & 90.8\% & 94.3\% & 90.5\% \\ \hline DeepSORT & 93.9\% & 95.9\% & 93.8\% \\ \hline Kalman Filter & 88.2\% & 91.2\% & 87.9\% \\ \hline \hline \end{tabular}
\end{table}