Appearance-Based Obstacle Detection with Monocular Color Vision
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Accessible spaces: navigating through a marked environment with a camera phone
Proceedings of the 9th international ACM SIGACCESS conference on Computers and accessibility
Assisting mobility of the disabled using space-identifying ubiquitous infrastructure
Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility
Iwalk: a lightweight navigation system for low-vision users
Proceedings of the 12th international ACM SIGACCESS conference on Computers and accessibility
A ratification of means: international law and assistive technology in the developing world
Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development
Exploration and avoidance of surrounding obstacles for the visually impaired
Proceedings of the 14th international ACM SIGACCESS conference on Computers and accessibility
Outdoor situation recognition using support vector machine for the blind and the visually impaired
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Intelligent situation awareness on the EYECANE
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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We demonstrate a novel assistive device which can help the visually impaired or blind people to gain more safe mobility, which is called as "EYECane". The EYECane is the white-cane with embedding a camera and a computer. It automatically detects obstacles and recommends some avoidable paths to the user through acoustic interface. For this, it is performed by three steps: Firstly, it extracts obstacles from image streaming using online background estimation, thereafter generates the occupancy grid map, which is given to neural network. Finally, the system notifies a user of an paths recommended by machine learning. To assess the effectiveness of the proposed EYECane, it was tested with 5 users and the results show that it can support more safe navigation, and diminish the practice and efforts to be adept in using the white cane.