This Small Business Innovation Research (SBIR) Phase I project seeks to implement state-of-the art image processing algorithms designed to identify objects amidst a complex background, which currently require high power computer processing resources, onto an embedded device, which would allow the capability to be used in small, portable, and affordable devices. The project further aims to include an obstacle avoidance algorithm to identify objects within a specific range. This high-risk research promises wide-ranging benefits, with the focus to be on creation of an assistive digital vision technology. The main objectives of the proposed effort include: 1) integrating a camera and sonar onto a commercially available embedded device; 2) development of an obstacle detection/avoidance algorithm for the sonar sensor and successfully implementing it into the embedded device; 3) customization of object identification / recognition algorithms for the camera sensor to implement them into the embedded device. Success will be evaluated by measuring performance of the resultant breadboard device in its ability to detect objects at various distances, and to recognize three common objects against a cluttered background. The goal and expected result is to exceed a probability of detection of 80%.
The broader impact/commercial potential of this project is enabling blind people to develop a more comprehensive mental picture of their surrounding and improving their situational awareness. There are 39 million visually impaired people living around the world. Guide dogs and white canes are the preferred mobility aids, but there is little else available to them that is both user-friendly and affordable. The envisaged technology will be compatible with the white cane and will alert the user to the presence of above ground obstacles, such as traffic and sign poles and overhanging objects. In addition, the object identification will further allow the user to develop a more comprehensive mental picture of his surroundings and improve his mobility. While other navigation support products are in the high hundreds to thousands of dollars, the technology proposed herein is expected to be commercialized in a product available for less than $200, thus allowing it to make a broad impact to the wide population of the visually impaired. The resultant technology that contains embedded camera and sonar technologies in a portable device may also contribute to the field of robotics and artificial intelligence, wherein a better understanding of the environment will support new decision-making algorithms.
