{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/vehicle-speed-estimation-using-computer","title":"Vehicle Speed Estimation Using Computer Vision And Evolutionary Camera Calibration","arxiv_id":null,"date":"2021-10-16","proceeding":"NeurIPS Workshop LatinX_in_AI 2021 12","authors":["Hector Mejia","Esteban Palomo","Ezequiel López-Rubio","Israel Pineda","Rigoberto Fonseca"],"abstract":"Currently, the standard for vehicle speed estimation is radar or lidar speed signs which can be costly to buy and maintain. However, most major cities already implement networks of traffic surveillance cameras that can be utilized for vehicle speed estimation using computer vision. This work implements such a system using homography estimation, YOLOv4 object detector, and an object tracker capable of vehicle speed estimation. The homography component uses world plane-image plane point correspondences, located by humans. Moreover, a new method is developed specifically for this use case, using the estimation of density evolutionary algorithm. It aims at correcting the points misalignment in between planes. 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 proposed homography 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 estimate speed distributions expected from vehicles on urban traffic and handle dynamic changes in vehicle speed.","url_abs":"https://openreview.net/forum?id=Pl7uHR-Oe6l","url_pdf":"https://openreview.net/pdf?id=Pl7uHR-Oe6l","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"vehicle-speed-estimation-using-computer","repo_url":"https://github.com/hector6298/titulacion_vehice_speed_estimation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"camera-calibration","task_name":"Camera Calibration"},{"task_slug":"homography-estimation","task_name":"Homography Estimation"},{"task_slug":"vehicle-speed-estimation","task_name":"Vehicle Speed Estimation"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottom-up-path-augmentation","method_name":"Bottom-up Path Augmentation"},{"method_slug":"cspdarknet53","method_name":"CSPDarknet53"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"cosine-annealing","method_name":"Cosine Annealing"},{"method_slug":"cutmix","method_name":"CutMix"},{"method_slug":"dropblock","method_name":"DropBlock"},{"method_slug":"fpn","method_name":"FPN"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"grid-sensitive","method_name":"Grid Sensitive"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"logistic-regression","method_name":"Logistic Regression"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"pafpn","method_name":"PAFPN"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"speed","method_name":"SPEED"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"spatial-pyramid-pooling","method_name":"Spatial Pyramid Pooling"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"},{"method_slug":"yolov3","method_name":"YOLOv3"},{"method_slug":"yolov4","method_name":"YOLOv4"},{"method_slug":"k-means-clustering","method_name":"k-Means Clustering"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}