1. Review the existing literature related to image processing–based traffic monitoring.

↓

2. Identify common traffic violations such as no helmet, red-light disobedience, lane encroachment, and illegal parking.

↓

3. Analyze gaps and limitations in current systems to establish the study’s scope and objectives.

PHASE II
DEVELOPMENT

4. Design the system architecture integrating image preprocessing, object detection, and notification modules.

↓

5. Integrate image processing techniques and machine learning models to detect specific traffic violations (e.g., no helmet, red-light disobedience, lane encroachment, illegal parking).

↓

6. Develop the notification mechanism to send real-time alerts to traffic authorities and concerned parties.

10. Conduct user acceptance evaluation using the Technology Acceptance Model (TAM).

↓

11. Assess the system in terms of Perceived Usefulness and Perceived Ease of Use.

↓

12. Refine and enhance the system’s features based on evaluation feedback.

7. Test the system’s capability to detect targeted traffic violations in controlled scenarios.

↓

8. Evaluate performance in terms of functionality and accuracy under different conditions.

↓

9. Analyze detection speed, reliability, and responsiveness of the notification system.