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

查看全文 作　　者：Asifa Mehmood [1]Qureshi;Naif Al [2]Mudawi;Mohammed [3]Alonazi;Samia Allaoua [4]Chelloug;Jeongmin [5]Park

机构地区：[1]Department of Creative Technologies,Air University,Islamabad,46000,Pakistan;[2]Department of Computer Science,College of Computer Science and Information System,Najran University,Najran,55461,Saudi Arabia;[3]Department of Information Systems,College of Computer Engineering and Sciences,Prince Sattam bin Abdulaziz University,Al-Kharj,16273,Saudi Arabia;[4]Department of Information Technology,College of Computer and Information Sciences,Princess Nourah bint Abdulrahman University,P.O.Box 84428,Riyadh,11671,Saudi Arabia;[5]Department of Computer Engineering,Tech University of Korea,Gyeonggi-do,15073,South Korea

出　　处：《Computers, Materials & Continua》索引2024年第78卷第3期,共19页

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. 摘　　要：Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To 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 matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved. 关键词：Unmanned Aerial Vehicles(UAV) aerial images DATASET object detection object tracking data elimination template matching blob detection SIFT VAID

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

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