Citation :

Sahira Vilakkumadathil, Velumani Thiyagarajan, M. Hemalatha, K. Kavitha, "Laplace Kernelized Adaptive Thresholding Segmentation Based Traffic Prediction in Intelligent Transport Systems," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 4, pp. 225-234, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I4P117

Abstract

Traffic congestion is a major challenge affecting the quality of life for millions of people worldwide. In order to improve the accuracy of the accurate traffic control prediction, A Laplace Kernel Filtering-based Hoover Index Adaptive Thresholding Segmentation (LKF-HIATS) method is introduced, used for accurate traffic control detection with minimal time complexity. The proposed method includes three different processes, namely vehicle image acquisition, image preprocessing, and segmentation. First, vehicle traffic images are collected from a dataset. After image acquisition, the Laplace kernelized Savitzky-Golay filter is applied to remove noise artifacts and enhance image contrast. The next process involves segmenting vehicle traffic images to separate the foreground and background. Followed by, the Hoover Index Adaptive Kittler-Illingworth Segmentation Algorithm is employed for threshold-based image segmentation. Experimental assessment is conducted with different evaluation metrics such as mean square error, peak signal-to-noise ratio, accuracy, and prediction time. The observed result shows the proposed LKF-HIATS method achieved better performance with higher accuracy and minimum time as well as error than the conventional segmentation methods.

Keywords

Intelligent transportation system, Traffic flow prediction, Laplace Kernelized Savitzky-Golay Filtering Technique, Hoover Index Adaptive Kittler-Illingworth Segmentation Algorithm.

References

[1] Mohammed Alonazi et al., “A Smart Traffic Control System based on Pixel-Labeling and SORT Tracker,” IEEE Access, vol. 11, pp. 80973-80985, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[2] Asifa Mehmood Qureshi et al., “Semantic Segmentation based Real‑Time Trafc Monitoring via Res‑UNet Classifer and Kalman Filter,” SN Computer Science, vol. 6, no. 1, pp. 1-17, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[3] Dang Minh Tan, and Le-Minh Kieu, “TRAMON: An Automated Traffic Monitoring System for High Density, Mixed and Lane-Free Traffic,” IATSS Research, vol. 47, no. 4, pp. 468-481, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[4] Adnan Ahmed Rafique et al., “Smart Traffic Monitoring through Pyramid Pooling Vehicle Detection and Filter-based Tracking on Aerial Images,” IEEE Access, vol. 11, pp. 2993- 3007, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[5] Guan-Wen Chen, Min-Te Sun, and Tsì-Uí İk, “PairingNet: A Multi-Frame based Vehicle Trajectory Prediction Deep Learning Network,” IEEE Access, vol. 11, pp. 29566-29575, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[6] Muhammad Ovais Yusuf et al., “Enhancing Vehicle Detection and Tracking in UAV Imagery: A Pixel Labeling and Particle Filter Approach,” IEEE Access, vol. 12, pp. 72896-72911, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[7] Muhammad Hassaan Ashraf et al., “HVD-Net: A Hybrid Vehicle Detection Network for Vision-based Vehicle Tracking and Speed Estimation,” Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 8, pp. 1-19, 2023.
[CrossRef] [Google Scholar] [Publisher Link] 

[8] Zhizhong Wu, “Deep Learning with Improved Metaheuristic Optimization for Traffic Flow Prediction,” Journal of Computer Science and Technology Studies, vol. 6, no. 4, pp. 47-53, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[9] Sang-Hyun Lee, Soomok Lee, and Ilsoo Yun, “Mosaic-Mixed Attention-based Unexpected Traffic Scene Classification,” IEEE Access, vol. 13, pp. 15712-15722, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[10] Nafiseh Zarei, Payman Moallem, and Mohammadreza Shams, “Real-Time Vehicle Detection using Segmentation-based Detection Network and Trajectory Prediction,” IET Computer Vision, vol. 18, no. 2, pp. 191-209, 2023.
[CrossRef] [Google Scholar] [Publisher Link] 

[11] Arindam Chaudhuri, “Smart Traffic Management of Vehicles using Faster R-CNN based Deep Learning Method,” Scientific Reports, vol. 14, no. 1, pp. 1-11, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[12] Deqi Huang et al., “A Lightweight Vehicle Detection Method Fusing GSConv and Coordinate Attention Mechanism,” Sensors, vol. 24, no. 8, pp. 1-15, 2024. 
[CrossRef] [Google Scholar] [Publisher Link]

[13] Anakhi Hazarika et al., “Edge ML Technique for Smart Traffic Management in Intelligent Transportation Systems,” IEEE Access, vol. 12, pp. 25443-25458, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[14] Ru An et al., “GC-YOLOv9: Innovative Smart City Traffic Monitoring Solution,” Alexandria Engineering Journal, vol. 106, pp. 277-287, 2024. 
[CrossRef] [Google Scholar] [Publisher Link]

[15] S. Kanagamalliga et al., “Traffic Management through Cutting-Edge Vehicle Detection, Recognition, and Tracking Innovations,” Procedia Computer Science, vol. 233, pp. 793-800, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[16] Guodong Su, and Hao Shu, “Traffic Flow Detection Method based on Improved SSD Algorithm for Intelligent Transportation System,” PLoS ONE, vol. 19, no. 3, pp. 1-21, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[17] A. Vikram et al., “Deep Learning based Vehicle Detection and Counting System for Intelligent Transportation,” Computer Systems Science and Engineering, vol. 48, no. 1, pp. 115-130, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[18] Bowie Liu et al., “A Graph-based Framework for Traffic Forecasting and Congestion Detection using Online Images from Multiple Cameras,” IEEE Access,  vol. 12, pp. 3756-3767, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[19] C. Mala, Manipriya Sankaranarayanan, and Snigdha Jain, “Traffic Density Estimation for Traffic Management Applications using Neural Networks,” International Journal of Intelligent Information Technologies, vol. 20,  no. 1, pp. 1-19, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[20] Wania Tahir et al., “Enhanced Vehicle Detection Mechanism for Traffic Management in Smart Cities,” Wireless Personal Communications, vol. 135, no. 4, pp. 1925-1945, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[21] Seungwoo Nham et al., “Scenario-based Segmentation: Traffic Image Segmentation by GNN based Driver’s Scenario,” IEEE Access, vol. 12, pp. 13088-13099, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[22] Gürkan Doğan, and Burhan Ergen, “A New CNN-based Semantic Object Segmentation for Autonomous Vehicles in Urban Traffic Scenes,” International Journal of Multimedia Information Retrieval, vol. 13, no. 1, pp. 1-11, 2024.
[CrossRef] [Google Scholar] [Publisher Link] 

[23] Asifa Mehmood Qureshi et al., “Intelligent Traffic Surveillance through Multi-Label Semantic Segmentation and Filter-based Tracking,” Computers, Materials and Continua, vol. 76, no. 3, pp. 3707-3725, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[24] Junwoo Lim et al., “Enhancing Real-Time Traffic Volume Prediction: A Two-Step Approach of Object Detection and Time Series Modeling,” IET Intelligent Transport System, vol. 18, no. 12, pp. 2744-2758, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[25] Xiexin Zou et al., “Deep Learning for Traffic Prediction and Trend Deviation Identification: A Case Study in Hong Kong,” Data Science for Transportation, vol. 6, no. 3, pp. 1-18, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[26] Fouzi Harrou et al., “Enhancing Road Traffic Flow Prediction with Improved Deep Learning using Wavelet Transforms,” Results in Engineering, vol. 23, pp. 1-14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[27] Anas Saleh Alkarim, Abdullah S. Al-Malaise Al-Ghamdi, and Mahmoud Ragab, “Ensemble Learning-based Algorithms for Traffic Flow Prediction in Smart Traffic Systems,” Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13090-13094, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[28] Erick Rodríguez-Esparza et al., “Optimizing Road Traffic Surveillance: A Robust Hyper-Heuristic Approach for Vehicle Segmentation,” IEEE Access, vol. 12, pp. 29503-29524, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[29] Younus Hasan Taher et al., “Filter for Traffic Congestion Prediction: Leveraging Traffic Control Signal Actions for Dynamic State Estimation,” IEEE Access, vol. 13, pp. 8140-8157, 2025.
[CrossRef] [Google Scholar] [Publisher Link]