Experimental studies on indoor sign recognition and classification

Previous works on outdoor traffic sign recognition and classification have been demonstrated useful to the driver assistant system and the possibility to the autonomous vehicles. This motivates our research on the assistance for visual impairment or visual disabled pedestrians in the indoor environment. In this paper, we build an indoor sign database and investigate the recognition and classification for the indoor sign problem. We adopt the classical techniques on extracting the features, including the principle component analysis (PCA), dense scale invariant feature transform (DSIFT), histogram of oriented gradients (HOG), and conduct the state-of-art classification techniques, such as the neural network (NN), support vector machine (SVM) and k-nearest neighbors (KNN). We provide the experimental results on this newly built database and also discuss the insight for the possibility of indoor navigation for the blind or visual-disabled people.

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