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Traffic Rule Violation Detection

Abstract:
Identification of traffic rule violators is a challenging task that requires overcoming various obstacles such as occlusion and illumination. However, it is crucial to ensure safety on Indian roads. In this study, we propose a comprehensive framework for detecting violations, notifying offenders, and storing violations for statistical analysis and decision-making regarding traffic regulations policies. Our method involves the use of object detection, specifically YOLO, to identify vehicles, followed by examining each vehicle for specific infractions like not wearing a helmet or violating crosswalks. We employ a CNN-based classifier to identify helmet violations and utilize the Instance Segmentation by Mask R-CNN architecture to detect crosswalk violations. Additionally, we employ Optical Character Recognition (OCR) to retrieve the car numbers of the violators for further action. Our fully autonomous system aids in enforcing strict traffic regulations, ensuring road safety.

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