A HYBRID FUZZY MORPHOLOGY AND CONNECTED COMPONENTS LABELING METHODS FOR VEHICLE DETECTION AND COUNTING SYSTEM

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

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Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

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VOLUME 9 , ISSUE 2 (June 2016) > List of articles

A HYBRID FUZZY MORPHOLOGY AND CONNECTED COMPONENTS LABELING METHODS FOR VEHICLE DETECTION AND COUNTING SYSTEM

Chastine Fatichah * / Joko Lianto Buliali * / Ahmad Saikhu * / Silvester Tena *

Keywords : connected component labeling, fuzzy morphology, image segmentation, vehicle detection.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 9, Issue 2, Pages 765-779, DOI: https://doi.org/10.21307/ijssis-2017-894

License : (CC BY-NC-ND 4.0)

Received Date : 30-November-2014 / Accepted: 10-April-2016 / Published Online: 01-June-2016

ARTICLE

ABSTRACT

A hybrid fuzzy morphology and connected components labeling method is proposed for detecting
and counting the number of vehicles in an image taken from a traffic monitoring camera. A fuzzy
morphology approach in image segmentation method is used in the system to achieve faster computation
time compared to the supervised learning. The connected components labeling method is combined with a
fuzzy morphology method to determine the region and number of objects in an image. The processing
phases in the proposed system are image preprocessing, image segmentation, and vehicle detection and
counting the number of vehicles. Images are captured from the traffic monitoring cameras installed in
highways. Results from testing phase using thirty images with varying brightness, contrast, and quality
taken from different cameras during daylight showed that the accuracy of the system in counting the
number of vehicles is 78.21%.

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