An unsupervised approach for traffic sign recognition based on bag-of-visual-words
Catur Supriyanto, Ardytha Luthfiarta, Junta Zeniarja · 2016
There are many ideas to enhance the safety riding. Advanced Driver Assistance System (ADAS) is a system to help the driver more safety. ADAS has a purpose to assist and direct the driver in order to improve traffic safety. Traffic sign recognition is one important part of ADAS. Traffic sign is a warning sign which placed at the side or above the road to provide detail road information to the driver. In this study, we propose an unsupervised approach for traffic sign recognition based on bag-of-visual-word model. Unsupervised approach does not require label data and training process for traffic sign recognition. It helps when we have many data without label. Our experiment was conducted with dataset from German Traffic Sign Recognition Benchmark (GTSRB). It is a public dataset for traffic sign recognition. The results show that, large number of visual word enable the proposed method produce high accuracy and good quality cluster. Although the accuracy in this study is still very low. The reason for that is each image only produces 3-4 number of keypoints. Small size of the images affect the small number of keypoints.