文章基本信息

标题：Vehicle detection using background subtraction and clustering algorithms
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作者：Puguh Budi Prakoso ; Yuslena Sari
期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2019
卷号：17
期号：3
页码：1393-1398
DOI：10.12928/telkomnika.v17i3.10144
出版社：Universitas Ahmad Dahlan
摘要：Traffic congestion has raised worldwide as a result of growing motorization, urbanization, and population. In fact, congestion reduces the efficiency of transportation infrastructure usage and increases travel time, air pollutions as well as fuel consumption. Then, Intelligent Transportation System (ITS) comes as a solution of this problem by implementing information technology and communications networks. One classical option of Intelligent Transportation Systems is video camera technology. Particularly, the video system has been applied to collect traffic data including vehicle detection and analysis. However, this application still has limitation when it has to deal with a complex traffic and environmental condition. Thus, the research proposes OTSU, FCM and K-means methods and their comparison in video image processing. OTSU is a classical algorithm used in image segmentation, which is able to cluster pixels into foreground and background. However, only FCM (Fuzzy C-Means) and K-means algorithms have been successfully applied to cluster pixels without supervision. Therefore, these methods seem to be more potential to generate the MSE values for defining a clearer threshold for background subtraction on a moving object with varying environmental conditions. Comparison of these methods is assessed from MSE and PSNR values. The best MSE result is demonstrated from K-means and a good PSNR is obtained from FCM. Thus, the application of the clustering algorithms in detection of moving objects in various condition is more promising.
其他摘要：Traffic congestion has raised worldwide as a result of growing motorization, urbanization, and population. In fact, congestion reduces the efficiency of transportation infrastructure usage and increases travel time, air pollutions as well as fuel consumption. Then, Intelligent Transportation System (ITS) comes as a solution of this problem by implementing information technology and communications networks. One classical option of Intelligent Transportation Systems is video camera technology. Particularly, the video system has been applied to collect traffic data including vehicle detection and analysis. However, this application still has limitation when it has to deal with a complex traffic and environmental condition. Thus, the research proposes OTSU, FCM and K-means methods and their comparison in video image processing. OTSU is a classical algorithm used in image segmentation, which is able to cluster pixels into foreground and background. However, only FCM (Fuzzy C-Means) and K-means algorithms have been successfully applied to cluster pixels without supervision. Therefore, these methods seem to be more potential to generate the MSE values for defining a clearer threshold for background subtraction on a moving object with varying environmental conditions. Comparison of these methods is assessed from MSE and PSNR values. The best MSE result is demonstrated from K-means and a good PSNR is obtained from FCM. Thus, the application of the clustering algorithms in detection of moving objects in various condition is more promising.
关键词：FCM;ITS;K-means;OTSU;vehicle detection