SEARCH WITHIN CONTENT
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
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%.
 W. Wang, 2009, “Reach on Sobel Operator for Vehicle Recognition,” Proceeding IEEE –
International Joint Conference on Artificial Intelligence.
 W. Shao, W. Yang, G. Liu, and J. Liu, 2012, “Car Detection from High-Resolution Aerial
Imagery Using Multiple Features,” Proceeding IEEE – IGARSS.
 W. Zhan and J. Yang, 2012, Real Time and Automatic Vehicle Type Recognition System
Design and Its Application,” International Conference on Mechanical Engineering and
Automation (ICMEA), Vol. 10.
 Z. Chen, T. Ellis and S. A. Velastin, September 2012, “Vehicle Detection, Tracking and
Classification in Urban Traffic,” Proceeding IEEE – Intelligent Transportation Systems,
Anchorage, Alaska, USA.
 Z. Zheng, X. Wang, G. Zhou, and L. Jianga, 2012, “Vehicle Detection Based on Morphology
From Highway Aerial Images,” Proceeding IEEE – IGARSS.
 F. Medeiros de S. Matos and R. Maria Cardoso R. de Souza, 2013, “Vehicle Image
Classification Method Using Edge Dimensions, SVM and Prototype,” Proceeding the
International Conference on Artificial Intelligence (ICAI).
 M. H. Malhi, M. H. Aslam, F. Saeed, O. Javed, and M. Fraz, 2011, “Vision Based Intelligent
Traffic Management System,” Proceeding IEEE – Frontiers of Information Technology.
. B. Sharma, V. K. Katiyar, A. K. Gupta, A. Singh, 2014, The Automated Vehicle Detection of
Highway Traffic Images by Differential Morphological Profile, Journal of Transportation
Technologies, Vol. 4, pp. 150-156.
 J. Arróspide, L. Salgado, 2014, “A Study of Feature Combination for Vehicle Detection Based
on Image Processing,” The Scientific World Journal, Vol. 2014, Article ID 196251.
 H. Wang, Y. Cai, L. Chen, 2014, “A Vehicle Detection Algorithm Based on Deep Belief
Network,” The Scientific World Journal, Vol. 2014, Article ID 647380.
 Y. Iwasaki, M. Misumi, T. Nakamiya, 2015, “Robust Vehicle Detection under Various
Environments to Realize Road Traffic Flow Surveillance Using an Infrared Thermal Camera,”
The Scientific World Journal, Vol. 2015, Article ID 947272.
 A. Suryatali, V. B. Dharmadhikari, 2015, “Computer vision based vehicle detection for toll
collection system using embedded Linux,” The Proceeding of International Conference on
Circuit, Power and Computing Technologies (ICCPCT), Nagercoil, 19-20 March 2015, pp. 1-7.
 A. P. Shukla, M. Saini, 2015, "Moving Object Tracking of Vehicle Detection”: A Concise
Review, International Journal of Signal Processing, Image Processing and Pattern Recognition,
Vol.8, No.3, pp.169-176.
 R. A. Hadi, G. Sulong, L. E. George, 2014, “Vehicle Detection And Tracking Techniques: A
Concise Review,” Signal & Image Processing: An International Journal (SIPIJ) Vol.5, No.1,
 T. Deng and H. Heijmans, 2012, “Grey-scale Morphology Based on Fuzzy Logic,” Journal of
Mathematical Imaging and Vision, Springer Netherlands, Vol. 16, no. 2, pp. 155-171.
 W. Pedrycz and A.V. Vasilakos, 1999, “Linguistic models and linguistic modeling,” IEEE
Transactions on Systems, Man, and Cybernetics, PART B: Cybernetics, Vol. 29, No. 6, pp.
 J. T. Yao, A.V. Vasilakos, and W. Pedrycz, 2013, “Granular Computing: Perspectives and
Challenges,” IEEE Transaction on Cybernetics, Vol. 43, No. 6, pp. 1977-1989.
 X. Ban, X. Z. Gao, X. Huang, and H. Yin, 2007, “Stability analysis of the simplest Takagi-
Sugeno fuzzy control system using circle criterion,” Information Science, Vol. 177, No. 20,
pp. 4387- 4409.
 A. Bouchet, J. Pastore, and V. Ballarin, 2007, “Segmentation of Medical Images using Fuzzy
Mathematical Morphology,” Journal of Computer Science & Technology, Vol. 7, No. 3, pp.
 C. Fatichah, M. L. Tangel, M. R. Widyanto, F. Dong, K. Hirota, 2012, “Interest-Based
Ordering for Fuzzy Morphology on White Blood Cell Image Segmentation,” Journal of
Advanced Computational Intelligence and Intelligent Informatics, Vol. 16, No. 1, pp. 76-86.
 C. Fatichah, D. Purwitasari, F. Effendy, and V. Hariadi, 2014, “Overlapping White Blood Cell
Segmentation And Counting On Microscopic Blood Cell Images,” International Journal on
Smart Sensing and Intelligent Systems, Vol. 7, No. 3, pp. 1271-1286.
 W. Chen, Y. Q. Shi, and G. Xuan, 2007, “Identifying computer graphics using HSV color
model and statistical moments of characteristic functions”, IEEE International Conference on
Multimedia and Expo (ICME07), Beijing, China, July 2-5.
 L. Shapiro, G. Stockman, 2002, “Computer Vision,” Prentice Hall. pp. 69–73.
 L. He, Y. Chao, K. Suzuki, 2008, “A Run-Based Two-Scan Labeling Algorithm,” IEEE
Transactions on Image Processing, Vol. 17, No. 5, pp. 749–756.