1 Affiliation : Faculty of Computers and Information, Beni-Suef University, Beni-Suef, Egypt
Email : firstname.lastname@example.org
2 Affiliation : Faculty of Computers and Information, Helwan University, Egypt
Email : Tahamahdy3000@yahoo.com
In the recent years the importance of automatic traffic control has increased due to the traffic jams problem especially in big cities for signal control and efficient traffic management. The input video is processed and analyzed to detect an ambulance vehicle. This article introduces a robust approach for ambulance detection and traffic counting approach using novel fuzzy Bat swarm optimization and different image processing techniques. The fuzzy Bat based optimization algorithm is used to generate a template of ambulance from the abstracted frames obtained from predefined ambulance samples. This is done by using a collection of Gabor filters that have been particularly customized for the ambulance detection problem in which filter selection is achieved to group filters that have similar characteristics. The fitness criterion based on Support Vector Machine (SVM) is used to evaluate the output filters. The proposed approach is composed of the following five fundamental building phases: Fuzzy Bat based optimization, image acquisition, object detection, counting the connected objects, and finally ambulance detection. One of the main advantages of the proposed approach is that key Gabor filters is obtained from the selected features (filters with highest membership values) which have a vital role in the ambulance detection phase. Experimental results show that the overall accuracy confirms that the performance of the proposed approach is high.
Ambulance detection; Gabor filters; Support Vector Machine (SVM); Swarm Intelligence; Image processing; Fuzzy logic; Computer vision.
 Z. Moutakki, I. Mohamed Ouloul,K. Afdel, and A. Amghar, “ Real- Time System Based on Feature Extraction for Vehicle Detection and Classification,” Transport and Telecommunication Journal, Vol. 19, No. 2, pp. 93–102, 2018.
 X. Xiang, M. Zhai,N. Lv and A. El Saddik, “Vehicle Counting Based on Vehicle Detection and Tracking from Aerial Videos,” Sensors 2018, 18, 2560; doi:10.3390/s18082560, 2018.
 N. Abbas, M. Tayyab, and M. Tahir Qadri, “Real Time Traffic Density Count using Image Processing,” International Journal of Computer Appli- cations, Vol. 83, No. 9, pp. 0975–8887, December 2013.
 H. Shen, S.M. Li, J.G. Mao, F.C. Bo, F.P. Li and H.P. Zhou, “A Robust Vehicle Tracking Approach Using Mean Shift Procedure,” Fifth International Conference on Information Assurance and Security(IAS), Vol. 2, pp. 741–744, Aug. 2009.
 S.Kapoor P. Gupta, P.Sharma, and P. Nath Singh, “Intelligent Ambulance with Automatic Traffic Control,” International Research Journal of Engineering andTechnology, Vol. 4 pp. 2395–0072, Jan. 2017.
 P. Patil and P.S. Nandyal, “Vehicle Detection and Traffic Assessment Using Images,” Advance in Electronic and Electric Engineering, Vol. 3, No. 8, pp.987–1000, 2013.
 M. Arora and V.K. Banga, “Real Time Traffic Light Control System,” 2nd International Conference on Electrical, Electronics and Civil Engineering (ICEECE2012), pp. 172–176, Singapore, April 28-29, 2012.
 P. Gupta, G.N. Purohit, and S. Pandey, “Traffic Load Computation for Real Time Traffic Signal Control,” International Journal of Engineering andAdvanced Technology, Vol. 2, Issue 4, April 2013.
 G. Guttikonda and C.S. Potumeraka, “Automated Traffic Sign Board Classification System,” International Journal on Computational Sciences andApplications (IJCSA), Vol. 5, No. 1, February 2015.
 G. Vigos, M. Papageorgioua, and Y. Wangb, “Real-time estimation of vehicle-count within signalized links,“ Journal of Transportation Research Part C: Emerging Technologies, Vol. 16, Issue 1, pp. 18–35, February
 T.H. Chen, J.L. Chen, C.H. Chen and C.M. Chang, “Vehicle detection and counting by using headlight information in the dark environment,” IEEE 2007 International Conference on Intelligent Information Hiding and Multimedia Signal Processing IIHMSP07, pp. 519–522., Kaohsiung, Taiwan, Nov.26-28, 2007.
 N.J. Uke and R.C. Thool, “Moving Vehicle Detection for Measuring Traffic Count Using OpenCV,” Journal of Automation and Control Engineering, Vol. 1, No. 4, December 2013.
 S.S. kanojia, “Realtime Traffic light control and Congestion avoidance system,” International Journal of Engineering Research and Applications (IJERA), Vol. 2, Issue. 2, pp. 925–929, Mar-Apr 2012.
 X. Yong, L. Zhang and Z. Song, “Real-time vehicle detection based on Haar features and Pairwise Geometrical Histograms,” Information and Automation (ICIA), IEEE International Conference, pp. 390–395, Shenzhen, 6-8 June 2011.
 Z. Sun, G. Bebis, and R. Miller, “On-road Vehicle Detection Using Evolutionary Gabor Filter Optimization,” IEEE Transactions on Intelligent Transportation Systems, Vol. 6, No.2, June 2005.
 F. Andronicus, Maheswaran, “Intelligent Ambulance Detection System, International Journal of Science,” Engineering and Technology Research (IJSETR), vol. 4, Issue. 5, May 2015.
 T. Weldon, W. Higgins and D. Dunn, Efficient gabor filter design for texture segmentation, Pattern Recognition, Vol. 29, No. 12, pp. 2005–2015,1996.
 Y. Hamamoto, S. Uchimura, M. Watanabe, T. Yasuda, Y. Mitani, and S. Tomota, “A gabor filter-based method for recognizing handwritten numerals,” Pattern Recognition, Vol. 31, No. 4, pp. 395–400, 1998.
 K. Chung, S. Kee, and S. Kim, Face recognition using independent com- ponent analysis og gabor filter responses, IAPR Workshop on machine visionapplications, pp. 331–334, 2000.
 M. Haghighat, S. Zonouz, and M. Abdel-Mottaleb, “CloudID: Trust- worthy cloud-based and cross-enterprise biometric identification,” Expert Systems with Applications, Vol. 42, pp. 7905–7916, 2015.
 R. Y. M. Nakamura, L. A. M. Pereira, K. A. Costa, D. Rodrigues, J.P. Papa, X.-S. Yang, BBA: A Binary Bat Algorithm for Feature Selection, Graphics, Patterns and Images (SIBGRAPI), 2012 25th SIBGRAPI Conference, Ouro Preto, Brazil, 22-25 Aug. 2012.
 X.S. Yang, “Bat algorithm for multi-objective optimization,” International Journal of Bio-Inspired Computation, vol. 3, NO. 5, pp. 267–274, 2011.  X.S. Yang, Bat Algorithm: Literature Review and Applications, International Journal of Bio-Inspired Computation, Vol. 5, No. 3, pp. 141–149, 2013.
 C. Stauffer and W.E.L Grimson, Adaptive background mixture models for real-time tracking, www.researchgate.net/publication/ 215722011 Adaptive background mixture models for real-time track- ing.1999.