Currently, the standard for vehicle speed estimation on urban areas and highways is radar or lidar speed signs which can be costly to buy, install and maintain. In addition, most major cities already implement networks of traffic surveillance cameras that can be utilized for vehicle speed estimation using computer vision. This work designs and implements such a system. Specifically, the proposed method is composed of three main components. First, we propose a camera calibration using homography with point correspondences between the image and world plane selected by the user. Second, a YOLOv4 object detector to locate the vehicles, and third, a modified object tracker that estimates vehicle speed. Moreover, for the calibration, a new method for camera calibration is developed specifically for this use case, using the estimation of density evolutionary algorithm. This methodology aims at correcting the misalignment between a point in the image plane and the world plane produced by the human operation. In addition, a basic direct linear transformation (DLT) and a random sample consensus robust version of DLT are implemented for comparison. Finally, the results show that the workflow using the proposed homography estimation method reduces the projection error from world to image point by 97\%, when compared to the other two methods, and the complete workflow can successfully register speed distributions expected from vehicles on urban traffic and handle dynamic changes in vehicle speed.