TRAFFIC SIGN DETECTION BASED ON SIMPLE XOR AND DISCRIMINATIVE FEATURES
DOI:
https://doi.org/10.11113/jt.v78.8908Keywords:
Color spaces, Image analysis, image segmentation, Traffic Sign Detection and Recognition (TSDR), exclusive OR logical operator (XOR), Learning Vector Quantization (LVQ), German Traffic Sign Detection Benchmark (GTSDB), Artificial Neural Networks (ANN).Abstract
Traffic Sign Detection (TSD) is an important application in computer vision. It plays a crucial role in driver assistance systems, and provides drivers with safety and precaution information. In this paper, in addition to detecting Traffic Signs (TSs), the proposed technique also recognizes the shape of the TS. The proposed technique consist of two stages. The first stage is an image segmentation technique that is based on Learning Vector Quantization (LVQ), which divides the image into six different color regions. The second stage is based on discriminative features (area, color, and aspect ratio) and the exclusive OR logical operator (XOR). The output is the location and shape of the TS. The proposed technique is applied on the German Traffic Sign Detection Benchmark (GTSDB), and achieves overall detection and shape matching of around 97% and 100% respectively. The testing speed is around 0.8 seconds per image on a mainstream PC, and the technique is coded using the Matlab toolbox.
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