# Estimation of Lane-Level Traffic Flow Using a Deep Learning Technique
## 2 Related Works
The procedure of object tracking generally includes the following steps [12]: first, input the video and execute the object detectors, such as YOLO, Faster R-CNN, or SSD, to detect the objects and obtain their detection frames. Then, obtain all the corresponding targets from the detection frames and extract their features, including apparent features and/or motion features. Thereafter, calculate the similarity between these objects in adjacent frames. Finally, after linking the same objects together, assign different objects with different IDs. For object tracking, SORT (Simple Online and Realtime Tracking) is combined with the Kalman filter and the Hungarian Algorithm, which enhances the efficiency of multiple object tracking. The Kalman filter algorithm is divided into two processes: prediction and update. The algorithm defines the motion state of the target as eight normally distributed vectors. When the target moves, the target frame position and the speed of the current frame are predicted based on the target frame and the speed of the previous frame. The predicted value and the observed value are obtained, the two normal distribution states are linearly weighted, and the current state of the system prediction is obtained. In the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Filters** & **Size** & **Output** \\ \hline Convolutional & 32 & \(3\times 3\) & \(256\times 256\) \\ Convolutional & 64 & \(3\times 3/2\) & \(128\times 128\) \\ \hline Convolutional & 32 & \(1\times 1\) & \\ Convolutional & 64 & \(3\times 3\) & \(128\times 128\) & \(1\times\) \\ Residual & & & \\ \hline Convolutional & 128 & \(3\times 3/2\) & \(64\times 64\) & \\ \hline Convolutional & 64 & \(1\times 1\) & \\ Convolutional & 128 & \(3\times 3\) & \(2\times 32\) & \(2\times\) \\ Residual & & & \(64\times 64\) & \\ \hline Convolutional & 256 & \(3\times 3/2\) & \(32\times 32\) & \\ \hline Convolutional & 128 & \(1\times 1\) & \\ Convolutional & 256 & \(3\times 3\) & \(8\times\) \\ Residual & & & \(32\times 32\) & \\ \hline Convolutional & 512 & \(3\times 3/2\) & \(16\times 16\) & \\ \hline Convolutional & 256 & \(1\times 1\) & \\ Convolutional & 512 & \(3\times 3\) & \(8\times 8\) & \\ Residual & & & \(8\times 8\) & \\ \hline Avgpool & & Global & \\ Connected & & 1000 & \\ Softmax & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The architecture of DarkNet-53 [9]. main multiple object tracking step, the similarity is calculated and the similarity matrix of the two frames before and after is obtained. The Hungarian algorithm solves the real matching goal of the two frames before and after solving this similarity matrix [13]. The object tracking procedure of SORT is shown in Figure 1.  
DeepSORT was developed from SORT. Information about the exterior of the objects was added in order to match objects in adjacent frames. The procedure of DeepSORT is as follows: first, the Kalman filter is used to predict the trajectory. Then, the Hungarian algorithm is used to match the predicted trajectory with the detections in the current frame (cascade matching and IOU matching). Thereafter, the Kalman filter is updated. DeepSORT can be applied in Multiple Object Tracking (MOT), which can assign each vehicle with different IDs. The object tracking procedure of DeepSORT is shown in Figure 2.  
[PERSON] et al. proposed a multiple object tracking system that has a detection model for target localization and an appearance embedding model for data association [15]. [PERSON] et al. applied a faster R-CNN detector and a SORT tracker in traffic flow estimation with data from a camera [16]. The application of YOLO and DeepSORT can be used in many multiple object tracking scenarios other than just traffic [17], including crowd control [18] and detecting and tracking moving obstacles [19]. This study applied artificial intelligence to improve traffic flow monitoring methods. Traffic flow prediction is one of the most important topics for self-driving cars, and various  
Figure 1: Object tracking procedure of SORT [14].