Published on: April 2026
WRONG SIDE VEHICLE DETECTION WITH LIVE CALLING ALERT , LIVE E-MAIL AND ONLINE PENALTY SYSTEM
Mansi Pagar Nilam Katore Sachin Raut Jay Rahane
AMRUTVAHUNI POLYTECHNIC ,SANGAMNER DEPARTMENT OF INFORMATION TECHNOLOGY
Article Status
Available Documents
Abstract
The rapid rise in traffic rule violations, such as wrong-side driving and triple seat riding, has become a major concern for urban safety management. Manual surveillance is inefficient, time- consuming, and prone to human error. To address this challenge, the proposed system, Wrong Side Vehicle and Triple Seat Detection in Traffic and Penalizing System using Deep Learning Model YOLO, leverages the power of computer vision and deep learning to automate the detection and penalization of traffic violators in real time. The system integrates high-definition CCTV or smart camera feeds with the YOLO (You Only Look Once) object detection framework, known for its remarkable accuracy and real-time performance. In the first stage, live traffic footage is captured and pre-processed through frame extraction, noise reduction, and image normalization techniques. These frames are then passed to the YOLO model, which has been trained on a diverse dataset containing multiple traffic scenarios, including two-wheelers, four-wheelers, and pedestrians in varying light and weather conditions. The model identifies vehicle positions and orientations to detect wrong-side movement, based on lane direction and road marking analysis. Simultaneously, it performs person counting on two-wheelers to identify triple seat violations by detecting and classifying human figures. Once a violation is detected, the system automatically captures the vehicle’s image, extracts the vehicle number plate using Optical Character Recognition and logs the incident in a centralized database. This approach ensures accuracy, speed, and scalability, reducingdependency on human monitoring while significantly improving road safety. The model can be deployed in smart city infrastructures, integrated with existing traffic management systems, and scaled across multiple intersections. By combining deep learning, computer vision, and automation, this project aims to enhance intelligent traffic regulation, minimize accidents, and encourage responsible driving behaviour. Ultimately, the proposed YOLO-based detection and penalizing system represents a major step toward AI-driven intelligent traffic monitoring and automated law enforcement in modern cities.
How to Cite this Paper
Pagar, M., Katore, N., Raut, S. & Rahane, J. (2026). Wrong Side Vehicle Detection with Live Calling Alert , Live E-Mail and Online Penalty System. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.405
Pagar, Mansi, et al.. "Wrong Side Vehicle Detection with Live Calling Alert , Live E-Mail and Online Penalty System." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.405.
Pagar, Mansi,Nilam Katore,Sachin Raut, and Jay Rahane. "Wrong Side Vehicle Detection with Live Calling Alert , Live E-Mail and Online Penalty System." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.405.
Search & Index
References
- [[1] A. M. Ali, A. Al-Qerem, M. R. Hassan, A. Abu-Khadrah and M. Jarrah, "Traffic Monitoring and Control System for Smart City Pollution Regulation Using IoT and Correlated Capsule Networks," in IEEE Access, vol. 13, pp. 46983-47003, 2025, doi: 10.1109/ACCESS.2025.3550966.
- D. A. Kumar, V. Ramachandran, M. Rashid, A. R. Javed,
- Islam and A. Al Hejaili, "An Intelligent Traffic Monitoring System in Congested Regions with Prioritization for Emergency Vehicle Using UAV Networks," in Tsinghua Science and Technology, vol. 30, no. 4, pp. 1387-1400, August 2025, doi: 10.26599/TST.2023.9010078.
- Bose, M. H. Kolekar, S. Nawale and D. Khut, "LoLTV: A Low Light Two-Wheeler Violation Dataset With Anomaly Detection Technique," in IEEE Access, vol. 11, pp. 124951- 124961, 2023, doi: 10.1109/ACCESS.2023.3329737.
- A. Abdelali, H. Derrouz, Y. Zennayi, R. O. H. Thami and
- Bourzeix, "Multiple Hypothesis Detection and Tracking Using Deep Learning for Video Traffic Surveillance," in IEEE Access, vol. 9, pp. 164282-164291, 2021, doi: 10.1109/ACCESS.2021.3133529.
- Mustafa and M. Karabatak, "Real Time Car Model and Plate Detection System by Using Deep Learning Architectures," in IEEE Access, vol. 12, pp. 107616-107630, 2024, doi: 10.1109/ACCESS.2024.3430857.
- Htet Htet Aung, P. Sangwongngam, R. Jintamethasawat and L. Wuttisittikulkij, "Fully Synthetic Pedestrian Anomaly Behavior Dataset Generation in Metaverse for Enhancing Autonomous Driving Object Detection," in IEEE Access, vol. 12, pp. 166630-166642, 2024, doi:
- Gao, Z. Li, D. Zhang, Y. Liu, J. Chen and Z. Lv, "Roadside Cross-Camera Vehicle Tracking Combining Visual and Spatial-Temporal Information for a Cloud Control System," in Journal of Intelligent and Connected Vehicles, vol. 7, no. 2, pp. 129-137, June 2024, doi: 10.26599/JICV.2023.9210034.
- Han, F. Liu, H. Liu, J. Deng, W. Bai and K. Li, "A Dynamic Multitarget Obstacle Tracking and Monitoring Method for Vehicle Routing Based on Multiple Constraint Composite Perception Association Filtering," in IEEE Access, vol. 12, pp. 115151-115170, 2024, doi: 10.1109/ACCESS.2024.3444054.
- Tang, F. Yan, B. Zou, K. Wang and C. Lv, "An Improved Kinematic Model Predictive Control for High-Speed Path Tracking of Autonomous Vehicles," in IEEE Access, vol. 8,
- 51400-51413, 2020, doi: 10.1109/ACCESS.2020.2980188.
- Z. Sun, R. Wang, Q. Ye, Z. Wei and B. Yan, "Investigation of Intelligent Vehicle Path Tracking Based on Longitudinal and Lateral Coordinated Control," in IEEE Access, vol. 8, pp. 105031-105046, 2020, doi: 10.1109/ACCESS.2020.2994437
Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Apr 15 2026
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.