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Determination And Identification Of Dangerously Lane Changing Vehicles In Traffic By Image Processing Techniques

Oluşturulma Tarihi: 11-06-2018

Niteleme Bilgileri

Tür: Makale

Yayınlanma Durumu: Yayınlanmış

Dosya Biçimi: Dosya Yok

Dil: İngilizce

Konu(lar): TEKNOLOJİ,

Yazar(lar): Şengül, Gökhan (Yazar), Bostan, Atila (Yazar),

Emeği Geçen(ler): Karakaya, Murat (Araştırma Sorumlusu),


Yayın Adı: International Journal of Scientific Research in Information Systems and Engineering (IJSRISE) Sayı: 1 Cilt: 3


Dosya:
Dosya Yok

Anahtar Kelimeler

determination, identification, processing techniques


Özet

 Due to increase of vehicle usage all around the world, the importance of safety driving in traffic is increasing. All of the countries around the world are taking actions to increase the safety driving habitats and decrease the number oftraffic accidents. One of the applied precautions is to put necessary automatic auditing mechanisms into service for controlling the drivers as they drive since reckless drivers may not obey many traffic rules. In this study, image andvideo processing based methods are applied to identify the dangerously lane changing vehicles/drivers in the traffic. The proposed method focuses on to detect three different violations in traffic: the vehicles frequently changingtraffic lanes, the vehicles changing lanes when it is forbidden, and the vehicles overtaking the other vehicles using the right lanes instead of left one. The proposed method is based on the image and video processing techniques. Itfirst detects the vehicles in video sequences, then tracks the vehicles in the following frames and determines the lane changes of the vehicles. In the vehicle detection phase an image subtraction method is used. In the vehicle trackingphase, Kalman filtering tracking algorithm is used. After determining the lane changes of the vehicles/drivers, a rule based decision system is used to find out the vehicles/drivers improperly changing lanes and those vehicles aremarked on the video. The proposed method is tested on the videos captured from real traffic environments and promising results are obtained.


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Kaynakça[1] Kumar, Pankaj, et al. "Framework for real-time behavior interpretation from traffic video."Intelligent Transportation Systems, IEEE Transactions on 6.1 (2005): 43-53.[2] Definition of MOBESE System (2016), Available Online at: https://tr.wikipedia.org/wiki/MOBESE[3] MOBESE CAMERA SYSTEMS, (2016), Available Online at: http://mobese.gen.tr[4] S. Kranthi, K. Pranathi, and A. Srisaila, “Automatic number plate recognition,” Int. J.Adv. Tech., vol. 2, no. 3, pp. 408–422, 2011.[5] C.-N. E. Anagnostopoulos, I. E. Anagnostopoulos, I. D. Psoroulas, V. Loumos,and E. Kayafas, “License plate recognition from still images and video sequences: A survey,”IEEE Trans. Intell. Transp. Syst., vol. 9, no. 3, pp. 377–391, Sep. 2008.[6] Murat Karakaya, Gökhan Åžengül, “Using ServiceOriented Architecture for Plate Recognition by Mobile Devices”, Girne American UniversityJournal of Social and Applied Sciences, 7, (2015): pp. 76-81[7] Kalman R E, “A new approach to linear filtering and prediction problems”, J. Basic Eng. V: 82,1960, 35–45[8] Ali, Nasser H; Hassan, Ghassan M., “Kalman filter tracking”, International Journal of ComputerApplications 89.9, 2014, pp: 15-22.[9] Y. T. Chan ; A.G.C. Hu ; J.B. Plant, “A Kalman Filter Based Tracking Scheme with InputEstimation”, IEEE Transactions on Aerospace and Electronic Systems,Volume: AES-15, Issue:2, March 1979, pp.237-244.[10] L. Matthies, T. Kanade, and R.Szeliski, “Kalman filter-based algorithms for estimating depth fromimage sequences”, Int J Comput Vision, 3: 209, 1989. doi:10.1007/BF00133032[11] Q. Gan ; C.J. Harris, “Comparison of twomeasurement fusion methods for Kalman-filterbased multisensor data fusion”, IEEETransactions on Aerospace and Electronic Systems,v Volume: 37, Issue: 1, Jan 2001, pp.273-279.[12] G. Åžengül, U Baysal, “An extended Kalman filtering approach for the estimation of humanhead tissue conductivities by using EEG data: a simulation study”, Physiological measurement 33(4), 2012, pp. 571-586


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