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What is best method of Traffic Sign Detection and Recognition? I review the popular traffic sign detection methods prevalent in recent literature, but don't know which way is best! I would like to use Color-based and shape-based detection methods.
I work image processing using opencv in visual studio c++.
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Traffic sign detection and recognition plays an important role in expert systems, such as traffic assistance driving systems and automatic driving systems. It instantly assists drivers or automatic driving systems in detecting and recognizing traffic signs effectively.
Signal detection theory is a method of differentiating a person's ability to discriminate the presence and absence of a stimulus (or different stimulus intensities) from the criterion the person uses to make responses to those stimuli.
Try this one: https://sites.google.com/site/mcvibot2011sep/
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Check dlib. The file example/train_object_detector.cpp* has some details on how this can be achieved. It uses a feature description technique called Histogram of Oriented Gradients (HOG).
Check the following links for a starting point:
* Note: don't just use the examples to train your detector! Read the files as a guide/tutorial! These example programs assume that you are trying to detect faces and make some improvements based on that (such as using image mirrors on training since faces are symmetric, which can be be disastrous for some signs).
Edit: Check my implementation of a traffic sign detector and classifier using dlib:
https://github.com/fabioperez/transito-cv
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