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Traffic Sign Recognition

14

Description

We use viola and Jones Detector [7] to detect traffic signs.

Algorithm

1) Features

Viola and Jones propose to use Harr-like feature extractor to extract features:

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Then we normalize the features to make it have a norm of 1:

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According to Viola et al., for a 24 by 24 feature window, we can extract approximately 160,000 dimensions of features.

2) Classification

160,000 dimensional feature is a large number. Viola et al. assume in the paper that only a small fraction of these features are useful and that the main problem is how to find useful features for this small fraction. Adaboost classifier is a suitable suitable choice, because this classifier has feature selection capabilities. The principle of Adaboost is to construct a strong classifier connected in parallel by multiple weak learners. Each weak learner multiplies its classification result by weights according to its own accuracy. The final output is the sum of all classifiers output. The classification accuracy of each weak classifier can be very low, but the accuracy of the whole strong classifier is very high.

Traditional adaboost classifier is still too timeconsuming. Viola et al. creatively modified the adaboost classifier to a cascade of several adaboost classifiers. Each classifier has high true positive rate (about 99%), while false positive rate is also high (about 50%). However, if we cascade 20 such small adaboost classifier, the false positive rate will become (50%)20 = 9.5 × 10−7 , while the true positive rate remains high.

3) Optimizing efficiency by computing integral image

 

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[7] Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on (Vol. 1, pp. I-511). IEEE.

Vehicle Lane Deviation Detection

Python code for Vehicle Make Detection by Convolutional Neural Networks

 

https://en.wikipedia.org/wiki/Traffic-sign_recognition

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