AUTOMATIC VEHICLE COUNTER SYSTEM BASED BLOB DETECTION FOR HIGHWAY SURVEILLANCE

  • Andri Agustav Wirabudi (1*) Telkom University
  • Nurwan Reza Fachrur Rozi (2) Telkom University
  • Heeji Han (3) Hanbat National University, South Korea

  • (*) Corresponding Author
Keywords: Vehicle counter, Blob Detection, OpenCV, Morphological Operation.

Abstract

The number of vehicles that increase every year has a major impact on the occurrence of congestion and accidents and causes a significant increase in the volume of vehicles, especially on the highway. With this increase, many officers find it difficult to be able to anticipate or supervise vehicles directly. The research that we made, entitled Automatic Vehicle Counter System Based on Blob Detection for Highway Surveillance Using OpenCV, is a solution to this problem because by utilizing image transformation it makes it easier for the system to be able to detect vehicles and identify the number of vehicles entering the lane. The results obtained show an accuracy value of 97.11% based on testing with 10 video samples, with a total of 1329 vehicles detected out of a total of 1362, meaning that the total error is only 3.02%.

Downloads

Download data is not yet available.

References

BPS Jakarta, “Statistik Transportasi DKI Jakarta 2021,” Yolanda Wilda Artati, vol. 2, no. 5, p. 60, 2021, [Online]. Available: https://nasional.tempo.co/read/1566965/integrasi-jadi-kunci-perubahan-transportasi-di-jakarta.

M.A. Sabri et al., 2018 International Conference on Intelligent Systems and Computer Vision (ISCV) : April 2-4, 2018, Faculty of Sciences Dhar El Mahraz (FSDM), Fez, Morocco.

T. W. Kuan, G. Xiao, Y. Wang, S. Chen, Y. Chen and J. -F. Wang, "Human Knowledge and Visual Intelligence on SDXtensionB," 2022 10th International Conference on Orange Technology (ICOT), Shanghai, China, 2022, pp. 1-4, doi: 10.1109/ICOT56925.2022.10008159.

P. Kumar & S. Sharma,"A Computer Vision Based on Vehicle Detection Counting System Using Sensor Security," 2021 4th International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE), Noida, India, 2021, pp. 624-629, doi: 10.1109/RDCAPE52977.2021.9633454.

R. A. Kumar*, D. S. T. Kumar, K. kalyan, and B. R. R. Reddy, “Vehicle Counting and Detection,” International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 8, pp. 763–766, Jun. 2020, doi: 10.35940/ijitee.H6696.069820.

M. B. Subaweh and E. P. Wibowo, “Implementation of Pixel Based Adaptive Segmenter method for tracking and counting vehicles in visual surveillance,” in 2016 International Conference on Informatics and Computing, ICIC 2016, Apr. 2017, pp. 1–5. doi: 10.1109/IAC.2016.7905679.

M. M. Dharmana and A. M.S., "Pre-diagnosis of Diabetic Retinopathy using Blob Detection," 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2020, pp. 98-101, doi: 10.1109/ICIRCA48905.2020.9183241.

U. Deteksi Kerusakan Jalan Yuslena Sari et al., “Penerapan Active Contour Model Pada Pengolahan Citra Penerapan Active Contour Model Pada Pengolahan Citra Untuk Deteksi Kerusakan Jalan (Application Of Active Contour Model On Image Processing For Detection Of Road Damage).”

C. Ma, G. Luo and K. Wang, "Concatenated and Connected Random Forests With Multiscale Patch Driven Active Contour Model for Automated Brain Tumor Segmentation of MR Images," in IEEE Transactions on Medical Imaging, vol. 37, no. 8, pp. 1943-1954, Aug. 2018, doi: 10.1109/TMI.2018.2805821.

S. Mandal, X. L. Dean-Ben, and D. Razansky, “Visual Quality Enhancement in Optoacoustic Tomography Using Active Contour Segmentation Priors,” IEEE Trans Med Imaging, vol. 35, no. 10, pp. 2209–2217, Oct. 2016, doi: 10.1109/TMI.2016.2553156.

K. Kumar, M. T. Talluri, B. Krishna and V. Karthikeyan, "A Novel Approach for Speed Estimation along with Vehicle Detection Counting," 2022 IEEE Students Conference on Engineering and Systems (SCES), Prayagraj, India, 2022, pp. 1-5, doi: 10.1109/SCES55490.2022.9887707.

I. K. E. Purnama, A. Zaini, B. N. Putra, and M. Hariadi, “Real time vehicle counter system for intelligent transportation system,” in International Conference on Instrumentation, Communication, Information Technology, and Biomedical Engineering 2009, ICICI-BME 2009, 2009. doi: 10.1109/ICICI-BME.2009.5417239.

G. A. Pina, E. U. Moya-Sanchez, A. Sanchez-Perez, and U. Cortes, “Automatic vehicle counting area creation based on vehicle Deep Learning detection and DBSCAN,” in Proceedings - IEEE International Conference on Cluster Computing, ICCC, 2022, vol. 2022-September, pp. 535–538. doi: 10.1109/CLUSTER51413.2022.00069.

J. F. Song, A. N. Bai, and R. Xue, “A reliable counting vehicles method in traffic flow monitoring,” in Proceedings - 4th International Congress on Image and Signal Processing, CISP 2011, 2011, vol. 1, pp. 522–524. doi: 10.1109/CISP.2011.6099921.

A. P. Renold and S. Chandrakala, “Convex-hull-based boundary detection in unattended wireless sensor networks,” IEEE Sensors Letters, vol. 1, no. 4, pp. 1–4, 2017. doi:10.1109/lsens.2017.2731200

H. Cevikalp, H. S. Yavuz and B. Triggs, "Face Recognition Based on Videos by Using Convex Hulls," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 12, pp. 4481-4495, Dec. 2020, doi: 10.1109/TCSVT.2019.2926165.

H. Cevikalp, H. S. Yavuz and B. Triggs, "Face Recognition Based on Videos by Using Convex Hulls," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 12, pp. 4481-4495, Dec. 2020, doi: 10.1109/TCSVT.2019.2926165.

L. Drumetz, J. Chanussot, C. Jutten, W. -K. Ma and A. Iwasaki, "Spectral Variability Aware Blind Hyperspectral Image Unmixing Based on Convex Geometry," in IEEE Transactions on Image Processing, vol. 29, pp. 4568-4582, 2020, doi: 10.1109/TIP.2020.2974062.

Aswan University. Faculty of Engineering and Institute of Electrical and Electronics Engineers, Proceedings of 2019 International Conference on Innovative Trends in Computer Engineering (ITCE) : February 2nd-4th, 2019, Aswan Mövenpick Hotel, Aswan, Egypt.

H. -J. Jeong, K. -S. Park and Y. -G. Ha, "Image Preprocessing for Efficient Training of YOLO Deep Learning Networks," 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), Shanghai, China, 2018, pp. 635-637, doi: 10.1109/BigComp.2018.00113.

C. Liu, Y. Tao, J. Liang, K. Li and Y. Chen, "Object Detection Based on YOLO Network," 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, 2018, pp. 799-803, doi: 10.1109/ITOEC.2018.8740604.

Published
2023-08-08
How to Cite
[1]
A. Wirabudi, N. Fachrur Rozi, and H. Han, “AUTOMATIC VEHICLE COUNTER SYSTEM BASED BLOB DETECTION FOR HIGHWAY SURVEILLANCE”, jitk, vol. 9, no. 1, pp. 89 - 95, Aug. 2023.
Article Metrics

Abstract viewed = 165 times
PDF downloaded = 105 times