Proposing a GPU based modified fuzzy nearest neighbor rule for traffic sign detection

The purpose of this study is introducing a graphical process unit (GPU) implementation of a modified fuzzy nearest neighbor rule useful for traffic sign detection (TSD). The new method tries to detect road signs using color information in order to locate regions of interest. The candidate regions of interest are obtained by color information. Afterward, candidate regions are used for making histogram of oriented gradient (HOG) feature. Finally, the features are fed into the GPU-based modified fuzzy nearest neighbor in order to detect traffic signs. The proposed rule modifies the way for fuzzification of query sample in terms of distances while the conventional fuzzy nearest neighbor (FNN) doesn´t care distance of local neighbors. The accuracy of the proposed method is compared with the state of the arts k-nearest neighbor (k-NN), FNN and support vector machine algorithms on the challenging German traffic sign detection benchmark (GTSDB) data set. Results indicate that the modified rule achieves good accuracy and is competitive compared to others.