Semantic Traffic Light Understanding for Visually Impaired Pedestrian A visually impaired pedestrian normally requires information of walkingstop signs with time to decide whether it is available to cross a road on crosswalk. Since traffic lights are installed in verity formats such as, man figure, color and numerical characters, it is complicate to simply understand the meaning. This paper proposes a method of semantic traffic light understand for a visually impaired pedestrian. In the method, a picture of traffic light is initially assumed to extract and type of traffic light are first classified into man figure, color and numerical characters by some classification tools such as signature. In man figure case, color and man gesture are classified and both information are decided base on Fuzzy logic. In the case of numerical characters, they are first enhanced and then classified by a classifier. The experiments have been performed with 200 samples of pedestrian traffic lights and results reveal acceptable accuracy.

Visual attention system based on Fuzzy Classifier to define priority of traffic signs for intelligent robotic vehicle navigation purposes In this paper we propose the use of Multiple Decision Attributes and Fuzzy Sets so that it is possible to classify the importance and priority of the detected traffic signs. The Analytic Hierarchy Process (AHP) was applied to calculate attribute weights, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to classify the traffic signs into their importance levels. The main objective is to contribute with a new system of perception and, through a knowledge base rules set, to be able to semantically relate the scene and to define which traffic sign is more important in a certain moment of navigation of the autonomous vehicle. The system of vision with 2D and 3D images must provide the a priori data of detection and classification of traffic signs for the fuzzy visual attention system, being able to detect the use of auxiliary signs (cones and emergency signs) and relates. Then, relate then to the detection of the navigable area in cases of road blocking (road at work, with a traffic accident, etc.) and give priority to the most important signs for the decision making of the vehicle. The results are promising and very satisfactory, we obtained an accuracy of 98.9% in the 2D classification task and 88% accuracy in the single frame 3D detection task.