A Blind assistance and navigation system using SIFT algorithm for indoor environments
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When blind people are trying to navigate the city streets, they can get assistance from a speaking GPS-enabled Smartphone, just like everyone else. Once they move indoor and lose access to the required satellite signals. While there are some indoor navigation systems that require things like radio-frequency tags to be strategically placed around the building, it‟s currently unrealistic to expect to find such systems installed in many places. However, we are going to propose blind assistance and navigation system for indoor environment. It required a computer with camera loaded up with digital images of the room. Features and key points of the images have been extracted using Scale Invariant Feature Transform (SIFT) algorithm from parent and child images. Database has been created with the key feature points and sound files for individual child images. Moreover, our Matching algorithm is modified K-D tree known as Best Bin First (BBF) method. It has been used to match the key points of parent images with database to recognize the desire child images. System has been developed to determine the desired object and guide them along it via verbal cues from the database. Experimental results are demonstrated this proposed method is accurate and reliable to solve the promising and challenging Blind Assistance and Navigation System using SIFT Algorithm for Indoor Environments.