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.