Abstract
Abstract: According to recent reports, traffic violations have mostly resulted in an increase in fatalities and injuries on Indian roads. Because manually identifying traffic violations takes time, an automatic computer vision-based object identification model was required. The fundamental idea behind this research is to identify many transgressions using a single video frame. To perform various activities, the security camera's input video stream is processed and annotated. COCO is the dataset utilized for red-light leaping, while Google pictures are annotated to provide the dataset for over boarding. Tensorboard is used to train the model and visualize its results. The criteria employed include precision, recall, Fmeasure, and Pmeasure. Red light skipping accuracy is 93%, and the over boarding mAP value is 0.5:0.95. This system makes extensive use of the video feed to detect various forms of breaches.
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