Nilakorn Seenouvong, U. Watchareeruetai, C. Nuthong, K. Khongsomboon, N. Ohnishi
{"title":"A computer vision based vehicle detection and counting system","authors":"Nilakorn Seenouvong, U. Watchareeruetai, C. Nuthong, K. Khongsomboon, N. Ohnishi","doi":"10.1109/KST.2016.7440510","DOIUrl":null,"url":null,"abstract":"A vehicle detection and counting system plays an important role in an intelligent transportation system, especially for traffic management. This paper proposes a video-based method for vehicle detection and counting system based on computer vision technology. The proposed method uses background subtraction technique to find foreground objects in a video sequence. In order to detect moving vehicles more accurately, several computer vision techniques, including thresholding, hole filling and adaptive morphology operations, are then applied. Finally, vehicle counting is done by using a virtual detection zone. Experimental results show that the accuracy of the proposed vehicle counting system is around 96%.","PeriodicalId":350687,"journal":{"name":"2016 8th International Conference on Knowledge and Smart Technology (KST)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"84","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 8th International Conference on Knowledge and Smart Technology (KST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KST.2016.7440510","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 84
Abstract
A vehicle detection and counting system plays an important role in an intelligent transportation system, especially for traffic management. This paper proposes a video-based method for vehicle detection and counting system based on computer vision technology. The proposed method uses background subtraction technique to find foreground objects in a video sequence. In order to detect moving vehicles more accurately, several computer vision techniques, including thresholding, hole filling and adaptive morphology operations, are then applied. Finally, vehicle counting is done by using a virtual detection zone. Experimental results show that the accuracy of the proposed vehicle counting system is around 96%.