An Algorithm for Road Sign Detection and Recognition using HOG and SVM
Recognizing traffic signs is a challenging problem; and it has captured the attention of the computer vision community for several decades. Essentially, traffic sign recognition is a multi-class classification problem that has become a real challenge for computer vision and machine learning techniques. We present a system which will detect the road sign with shape detection technique. Further recognition of road sign is carried out using Histogram Oriented Gradient and Support Vector Machine. In this paper all red colour Indian road signs are effectively detected. Although many machine learning approaches are used for traffic sign recognition, they are primarily used for classification, not feature design. Identifying rich features using modern machine learning methods has recently attracted attention and has achieved success in many benchmarks. However these approaches have not been fully implemented in the traffic sign recognition problem. In this paper, we propose a new approach to tackle the traffic sign recognition problem. Recognition part will focus on Circular and Triangular road signs. Results show a high success rate. From these results, we can conclude that the proposed algorithm is invariant to translation, rotation, scale, and, in many situations, even to partial occlusions.
Keywords: Intelligent transport systems, YCbCr colour space, extent, shape detection, projected area, HOG, SVM
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