<![CDATA[Traffic is an important problem because traffic is medium to move from one place to another. When there is a traffic problem or become stuck, then people's mobility will have problem too. Traffic density data is an important role to understand traffic condition. Currently, in order to obtain traffic density still do it in conventional way, that is with some people to count each vehicles passing by at certain time. The purpose of this research is to apply algorithm Background Subtraction and Blob Detection to determine total vehicles and test the result of the vehicle counter system. Background Subtraction is used to process segmentation to separate an object with the background by counting difference between each pixel and use a threshold to make two dominant group of pixel. The method used to determine object position and total vehicles by Blob Detection and Background Subtraction. Testing done with twenty image by taking smallest error value as a best evaluation. The performance of precision is 93.44%, recall is 77.03% and accuracy is 73.75%. The value of precision, recall and accuracy needs to be increased again by adding test parameters and multiplying datasets with different conditions. The results show that the Blob Detection and Background Subtraction methods can give pretty good results when blob between vehicles is spaced. This method does not provide good results when used in heavy traffic conditions with vehicle bodies sticking together.]]>
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