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Project title: Traffic Sign Detection and Recognition In this project, our aim is to detect traffic signs using combination of traditional computer vision techniques. Four

Project title: Traffic Sign Detection and Recognition
In this project, our aim is to detect traffic signs using combination of traditional computer vision techniques. Four tasks are as follows:
Color Segmentation: Identify regions of interest (ROI) likely containing traffic signs based on pre-defined color ranges (e.g., red for stop signs, yellow for caution signs).
Edge Detection: Apply edge detection algorithms (e.g., Canny Edge Detection) to extract prominent edges in the ROI to better define the sign's shape.
Template Matching: Prepare templates of common traffic signs (scaled and normalized for size variations). Use template matching techniques to compare the ROI's edges with the templates and identify potential matches.
Shape Analysis: Implement simple shape analysis (e.g., aspect ratio, corner count) to further refine potential matches and eliminate false positives based on non-standard shapes.
Note that you should not use deep learning based techniques
There will be a bounus point if you run it in real-time (and get high FPS with the same accuracy) using a connected camera to a laptop
Completely AI generated projects will not get the grade (AI detector will be used). You can use AI in different steps.

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