## 2 State of the Art  
Today many works have been developed to determine the velocity of objects using different techniques and methods. The following represent the most representative works considered for the realization of this work. In Table 1 we show a comparison of papers that described the type of hardware used to obtain video images, one, two, or stereo vision. We describe the method used to obtain the speed estimation, classified according to physical formulas, pixels proportion, or machine learning algorithms (e.g., YOLO v2, CNN). We also present the precision of each work, allowing us to observe the main differences and that the speed estimation is still an open problem to solve.  
[PERSON] and [PERSON] in [27] and [PERSON] et al. in [28] used a video camera to identify the road lines, which were used as a reference point to estimate the distance traveled by the vehicles and thus calculate their speed.  
Authors such as [PERSON] et al. [21] uses a camera and the vehicle centroid as a reference point. On the other hand, [PERSON] in [29] takes the vehicle centroid as a reference, but he uses a drone with two LiDAR systems to detect vehicles as they pass through two points to calculate the speed at which they pass.  
Other authors use a video camera to process the images, focusing only on the vehicles' area. The complete process eliminates the entire background leaving only the vehicles [30; 31; 32].  
Artificial intelligence algorithms were used in [15; 22; 33; 34; 35; 36; 37; 38], ranging from the simplest ones such as multilayer perceptron and convolutional neural networks to networks such as Faster R-CNN, SqueezeDet, PWC-Net, AlexNet, and FlowNet.  
The work of [PERSON] et al. [15] presents YOLO as a neural network able to identify multiple objects in an image with a single inference. This work is not focused on vehicular traffic analysis. However, in our work, we use YOLO v3 to detect a vehicle while estimating the vehicle's speed.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Hardware** & **Method** & **Precision** \\ \hline [PERSON] et al. [39] & two & Physical & \textless{}3 km/h \\  & Cameras & Formula & \\ \hline [PERSON] et al., 2019 [40] & Stereo & Physical & \(-\)1.6, +1.1 km/h \\  & Cameras & Formula & \\ \hline [PERSON] et al., 2020 [41] & Stereo & Physical & \(-\)0.72, +1.17 km/h \\  & Cameras & Formula & \\ \hline [PERSON] et al., 2021 [42] & Stereo & Physical & \(-\)0.9, +1.06 km/h \\  & Cameras & Formula & \\ \hline [PERSON] et al. [43] & Camera & Physical & 0.59 km/h \\  & Cameras & Formula & \\ \hline [PERSON] et al. [23] & Camera & Physical & 1.32 km/h \\  & Cameras & Formula & \\ \hline [PERSON] et al. [28] & Camera & Pixels & 3 km/h \\  & Cameras & Proportions & \\ \hline [PERSON] et al. [21] & Camera & Physical & 98\% accuracy \\ \hline [PERSON] et al. [29] & Dron LiDAR & Physical & 1.31 km/h \\ \hline [PERSON] et al. [30] & Camera & Physical & 2.3 km/h \\  & Cameras & Formula & \\ \hline [PERSON] et al. [31] & Camera & Projective transformation & 97.01\% No Shadow \\  & Cameras & Subpixel Stereo Matching & \(\pm\)6\% \\ \hline [PERSON] et al. [22] \({}^{\star}\) & Camera & YOLOv2 & 2.25 km/h \\  & GNSS IMU & Faster R-CNN & \\ \hline [PERSON] et al. [33] \({}^{\star}\) & Camera & 3D CNN & 2.71 km/h \\ \hline \hline \end{tabular}
\end{table}
Table 1: State of the art summary. (* Machine learning works).