Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

作     者：Asifa Mehmood Qureshi Naif Al Mudawi Mohammed Alonazi Samia Allaoua Chelloug Jeongmin Park 

作者机构：Department of Creative TechnologiesAir UniversityIslamabad46000Pakistan Department of Computer ScienceCollege of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information SystemsCollege of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information TechnologyCollege of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer EngineeringTech University of KoreaGyeonggi-do15073South Korea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第78卷第3期

页      面：3683-3701页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 0823[工学-交通运输工程] 

基　　金：supported by a grant from the Basic Science Research Program through the National Research Foundation(NRF)(2021R1F1A1063634)funded by the Ministry of Science and ICT(MSIT),Republic of Korea The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Group Funding Program Grant Code(NU/RG/SERC/13/40) Also,the authors are thankful to Prince Satam bin Abdulaziz University for supporting this study via funding from Prince Satam bin Abdulaziz University project number(PSAU/2024/R/1445) This work was also supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R54) Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia 

主　　题：Unmanned Aerial Vehicles(UAV) aerial images dataset object detection object tracking data elimination template matching blob detection SIFT VAID 

摘      要：Road traffic monitoring is an imperative topic widely discussed among *** used to monitor traffic frequently rely on cameras mounted on bridges or ***,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger *** this end,different models have shown the ability to recognize and track ***,these methods are not mature enough to produce accurate results in complex road ***,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image *** extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the *** masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring ***,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise *** preprocessing,the blob detection algorithm helped detect the *** of varying sizes have been detected by implementing a dynamic thresholding *** was done on the first image of every ***,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching *** further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple *** accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been *** the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.