Research Article
Vehicle Detection and Counting to Identify Traffic Density in The Intersection of Road Using Image Processing
@INPROCEEDINGS{10.4108/eai.2-5-2019.2284706, author={Fitria Claudya Lahinta and Zahir Zainuddin and Syafruddin Syarif}, title={Vehicle Detection and Counting to Identify Traffic Density in The Intersection of Road Using Image Processing}, proceedings={1st International Conference on Science and Technology, ICOST 2019, 2-3 May, Makassar, Indonesia}, publisher={EAI}, proceedings_a={ICOST}, year={2019}, month={6}, keywords={gaussian mixture model (gmm) morphological operation (mo) vehicle detection counting vehicle teling intersection}, doi={10.4108/eai.2-5-2019.2284706} }- Fitria Claudya Lahinta
Zahir Zainuddin
Syafruddin Syarif
Year: 2019
Vehicle Detection and Counting to Identify Traffic Density in The Intersection of Road Using Image Processing
ICOST
EAI
DOI: 10.4108/eai.2-5-2019.2284706
Abstract
Vehicle density information for traffic regulation including the timing of traffic lights is still very minimal. This study aims to calculate the number of vehicles at an intersection then classify the density level of each road segment. The detection process begins with taking video from Teling intersection of Manado City, Indonesia. Video processed using the Gaussian Mixture Model (GMM) algorithm and Morphological Operation (MO) to detect vehicles object in the form of BLOB (Binary Large Object). The results of the feature extraction are calculated to get the number of vehicles from the specified Region of Interest (ROI). The results of counting vehicles are classified according to the density level to be able to compare the level of congestion on each road segment. The results of the proposed system accuracy is 90.9% for the calculation of vehicles on the road. This research is expected to be implemented in Smart Traffic Light.