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.
|Title of host publication||ICCAS 2015 - 2015 15th International Conference on Control, Automation and Systems, Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|Publication status||Published - 2015 Dec 23|
|Event||15th International Conference on Control, Automation and Systems, ICCAS 2015 - Busan, Korea, Republic of|
Duration: 2015 Oct 13 → 2015 Oct 16
|Name||ICCAS 2015 - 2015 15th International Conference on Control, Automation and Systems, Proceedings|
|Other||15th International Conference on Control, Automation and Systems, ICCAS 2015|
|Country||Korea, Republic of|
|Period||15/10/13 → 15/10/16|
Bibliographical notePublisher Copyright:
© 2015 Institute of Control, Robotics and Systems - ICROS.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering