# Highly Accurate Deep Learning Models for Estimating Traffic Characteristics from Video Data
## 6 Conclusions  
This paper developed a deep learning video processing method to estimate highly disaggregated traffic characteristics from video data. The unique features and contributions of the paper include:  
1. Contrary to other existing techniques, such as drawing virtual boxes or setting reference lines that should be measured in advance, this method does not need any calibration that requires measuring in advance, meaning that this can be considered a 'generic' approach. 2. Important parameters can be estimated that could not be estimated by employing existing camera-based video processing methods. This includes lane change behaviour, speed variance, detecting fire, and vehicle trajectories. 3. This technique could offer higher accuracy, especially in estimating individual vehicle speed. Therefore, it not only can be applied to real-time videos but can also be used to read existing videos regardless of camera pose and recording angle. 4. This paper optimises a fairness multi-object tracking (FairMOT) network by using CSPDarknet53 to strengthen typical FairMOT ability in detecting and tracking traffic scenarios, where a bounding box was obtained by subjecting an encoder-decoder network within FairMOT predictions to non-max suppression, integrating CSPDarknet53 and retaining DLA structure in FairMOT to fuse aggregated features. The modified FairMOT associates the same vehicle across different frames, allowing the obtaining of screen-space distance offset per vehicle [33]. 5. A proportional integral derivative (PID) controller was incorporated as a kernel function \(\sigma\) to suppress sporadic noise in speed caused by precision limitation due to low resolution. 6. Finally, a world-space speed (i.e., traveling speed of vehicles) was obtained by transforming the image-space offset with a projection matrix concluded from pre-labelled road markings. The detection and estimation accuracy for the proposed model in the tested scenario has been validated by ground truth data that were identified by observing recorded videos from roadside surveillance cameras in the past three years with professional justification of experts and the collection of actual vehicle speed with reference to LiDAR, radar, and speed data returned from the Gaode Map Company. Results showed that the proposed method can successfully detect abnormal scenarios, such as accidents, abnormal parking, pedestrians, unexpected objects, traffic congestion, and vehicle fires. Results also demonstrated that speed detection accuracy on different distance ranges and view angles varies. In the most prevalent parallel view, the speed loss is minimized within 50 m, which amounts to a 5% speed estimate difference per vehicle in freeway analysis. In addition, the PID implementation of \(\sigma\) effectively suppresses speed noise by 36.62%. A real-time abnormal traffic condition alarming system can be built with the application of the deep learning computer vision technique along with judgment logic to detect abnormal traffic incidents and set alarms for unprecedented conditions, such as vehicle speed, lane traffic flow, vehicle types, vehicle trajectory, accidents, abnormal parking, human walking, unexpected objects, traffic congestion, vehicle fires, etc. However, a real-time alarm system demands sufficient GPU computing power and an internet connection to proceed with real-time data. This system can be established in the control centre of the freeway management department to monitor traffic conditions, and corresponding prevention methods can be devised to cope with emergency incidents ahead of time. The developed algorithm makes good use of roadside cameras. A great number of roadway traffic operational data could provide precious information for freeway management. By making use of prompt traffic information, researchers can build an intelligent alert system that cannot only inform the control management centre of current traffic operating status (automatic surveillance) but also build a forecasting system to predict near future crash happening.