* Corresponding Author
Hide
Show all
Estimation of Average Car Speed Using the Haar-Like Feature and Correlation Tracker Method
https://doi.org/10.22146/ijccs.57262 Muhammad Dzulfikar Fauzi(1*), Agfianto Eko Putra(2), Wahyono Wahyono(3)
(1) Master Program in Computer Science, FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(3) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author
Abstract
The speed of a car traveling on the road can generally be estimated by using a speed gun. Efforts are needed to use CCTV (closed circuit television) as a tool that can be used to estimate the speed of the car so as to ease the burden on the road operator to estimate the speed of the car. This study discusses the estimated average speed of the car with the Haar-like Feature method used to detect the car, then the detection results are tracked using Correlatin Tracker to track the movement of objects that have been detected and calculate the distance of movement from the car, so that the speed of the car detected in video can be estimated. The results of the estimated average speed compared with the results of taking speed with a speed gun so that an error is obtained by MAE testing of 5,55 km / hour and the resulting standard deviation is 4,61 km / hour, thus it can be concluded that the system is made valid and can be used by road organizers to monitor the average speed of a car.
Keywords
Full Text:
PDFReferences
[1] K. Zheng, Y. Zhao, J. Gu, and Q. Hu, “License Plate Detection Using Haar-like Features and Histogram of Oriented Gradients,” 2012 IEEE Int. Symp. Ind. Electron., no. November, pp. 1502–1505, 2012.
[2] A. Lazaro, J. L. Buliali, and B. Amaliah, “Deteksi Jenis Kendaraan di Jalan Menggunakan OpenCV,” J. Tek. ITS Vol.6, vol. 6, no. 2, 2017.
[3] M. Irfan, B. A. A. Subondo, and I. Candradewi, “Sistem Klasifikasi Kendaraan Berbasis Pengolahan Citra Digital dengan Metode Multilayer Perceptron,” IJEIS, vol. 7, no. 2, pp. 139–148, 2017.
[4] N. Mer and M. P. Bhamare, “Image processing based Tracking and Counting Vehicles,” 2019 3rd Int. Conf. Electron. Commun. Aerosp. Technol., pp. 335–339, 2019.
[5] M. Jalalat, M. Nejati, and A. Majidi, “Vehicle detection and speed estimation using cascade classifier and sub-pixel stereo matching,” Proc. - 2016 2nd Int. Conf. Signal Process. Intell. Syst. ICSPIS 2016, pp. 1–5, 2017.
[6] J. J. P. Belen, J. C. V. Caysido, A. B. Llena, E. J. O. Samonte, G. N. Vicente, and E. A. Roxas, “Vision based classification and speed estimation of vehicles using forward camera,” Proc. - 2018 IEEE 14th Int. Colloq. Signal Process. its Appl. CSPA 2018, no. March, pp. 227–232, 2018.
[7] X. Gu, Z. Chen, T. Ma, F. A. N. Li, and L. Yan, “REAL-TIME VEHICLE DETECTION AND TRACKING USING DEEP NEURAL NETWORKS,” 2016 13th Int. Comput. Conf. Wavelet Act. Media Technol. Inf. Process., pp. 167–170, 2016.
[8] M. Danelljan, G. Häger, and M. Felsberg, “Accurate Scale Estimation for Robust Visual Tracking,” Proc. Br. Mach. Vis. Conf. BMVC, p. 11, 2014.
[9] N. H. Tsani, I. B. Dirgantoro, and A. L. Prasasti, “Impelementasi Deteksi Kecepatan Kendaraan Menggunakan Kamera Webcam dengan Metode Frame Difference,” e-Preceeding Eng., vol. 4, no. 2, pp. 2373–2381, 2017.
[10] J. Sangeetha and V. S. J. Prakash, Improved Feature-Specific Collaborative Filtering Model for the Aspect-Opinion Based Product Recommendation. Springer Singapore, 2019.
[11] A. Pande and B. Wolshon, TRAFFIC ENGINEERING HANDBOOK, Seventh Ed. Hoboken, New Jersey: John Wiley & Sons, Inc, 2016.
DOI: https://doi.org/10.22146/ijccs.57262
Article Metrics
Abstract views : 2054
| 
views : 2008
Refbacks
- There are currently no refbacks.
Copyright (c) 2020 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1