###### Abstract
Over Roadway-based, unlike previous devices, are installed in separate infrastructures, so the road is not modified. Among these devices are cameras [8], infrared sensors, ultrasonic sensors, satellites, and UAVs [9]. Side Roadway-based vehicle classifications do not invade the roadway and are installed on one side. Among them we have magnetic sensors [10], LIDAR [11], Wi-Fi [12], etc. An important assumption in this work is that number of cameras installed worldwide has been growing in recent years. Estimating the speed limits allows preserving road safety. The most relevant parameter for traffic monitoring is the car's speed because when used with vehicle counting, it allows determining the behavior of motorized mobility in cities. Therefore, accurately estimating vehicle speed is an open problem for Intelligent Transport Systems (ITS). This task has been addressed by using sensors like radars, laser, and fixed infrastructure, but these sensors are affected by weather conditions and noises or occlusions. When cameras are considered to obtain the vehicle speed, we must consider the translation of a 3D plane into a 2D discrete plane. This intrinsic limitation results in a digital representation whose accuracy follows an inverse-square law; its quantity is inversely proportional to the square of the distance from the camera to the vehicle. When a monocular camera is used to obtain video images to estimate the distance of the vehicles, usually a set of constraints are considered, such as flat road, including methods based on the homography and the use of augmented intrusion lines, patterns or regions, or prior knowledge about the actual dimensions of some of the objects (e.g., the license plates or the size of the vehicles) [13]. The problem of carrying out the speed estimation with a monocular camera is that we have to synchronize data recording, tracking, and detecting the vehicles over the road considering the lanes and distance between cars, among other problems. To identify the location of vehicles on each frame, we must first distinguish objects from the background. This procedure is known as image segmentation. It can be accomplished by subtracting each frame from the background image, but it is highly resource-consuming and generates a noncontiguous object [14]. Nevertheless, new developments in object detection, such as YOLO [15], Yolov2 [16] and Yolov3 [17], have been used in several context with good accuracy, such as [18; 19]. Yolo considers the frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. [15]  
Other works demonstrated that the use of consecutive [20; 21; 22] or nonconsecutive [23; 24; 25] images to estimate speed impacts considerably the accuracy. Hence, the problem is How to integrate all available measurements (instantaneous, mean, optimal, etc.) to better estimate the speed without affecting the final accuracy? An optical approach has been used to determine the velocity of vehicles in movement with a monocular camera, as is the case of drones and autonomous vehicles. For instance, [PERSON] et al. [26] propose an algorithm that estimates the distance and velocity of the car based on optical flow measured from a monocular camera and the knowledge of control inputs. They extended the Kalman filter to state the estimations and used them for landing control. The optical flow refers to the apparent visual motion of objects in a scene relative to an observer, allowing to determine how fast the camera moves and how close it is relative to the things it sees. In contrast, [PERSON] et al. [14] rectify image sequences captured by uncalibrated cameras; their method automatically estimates two vanishing points using lines from the image plane.