1. A Novel Approach of Assisting the Visually Impaired to Navigate Path and Avoiding Obstacle-Collisions

2. Outline  Introduction  Method Camera image pre-processing Multi-dimensional makes segmentation Edge Detection Template Matching Traversal area mapping  Results  Conclusion

3. Introduction  There is no alternative of visual percepts to brain for accomplishing any easy to complex problem solution.  We propose a real-time solution which utilizes image processing methodology and low cost hardware to support the visually impaired for every day path navigation and obstacle avoidance.

4. Introduction  This problem can be solved by modern day navigation aids or Electronic Travel Aids (ETA’s). Many costly devices exist to assist visually impaired people for navigation.  The main aim of this study is to make use of only camera as sensor to achieve better results for aided navigational research.

5. Method  The main challenge is the creation of an algorithm that is adaptable to variable environmental conditions while utilizing the least possible computational resource that would facilitate execution on a low-cost processing unit.

6. Method  Fig. 1. Traversable area detection methodology.

7. Camera image pre-processing  Noise-filtering  Converting the RGB color-space to HSL  The saturation channel is extracted and further resized to a coarse 64 X 48 saturation intensity map by Gaussian pyramid decomposition of the 320 X 240 input image.

8. Camera image pre-processing  Fig. 2. Image analysis into Saturation channel

9. Multi-dimensional pyramidal segmentation  Gaussian 、 Laplacian  In this method, one builds an image pyramid and then associates to it a system of parent–child relations between pixels at level G i+1 and the corresponding reduced pixel at level G i.

10. Multi-dimensional pyramidal segmentation  Fig. 3.Segmentation Results

11. Edge Detection  An edge in an image may point in a variety of directions, so this algorithm uses four filters to detect horizontal, vertical and diagonal edges in the blurred image.  The edge detection operator returns a value for the first derivative in the horizontal direction (Gx) and the vertical direction (Gy).

12. Edge Detection  The edge direction angle is rounded to one of four angles representing vertical, horizontal and the two diagonals (0, 45, 90 and 135 degrees for example).

13. Edge Detection  Fig. 4. Edge detection after segmentation

14. Template Matching  The current approach also adopts this idea since the "safe" window can always be validated by low-cost active shortrange sensors such as ultrasonic or infrared.

15. Template Matching  Fig. 5. Template Matching

16. Template Matching  Some challenges are there which cannot be solved by this approach as some rational decision making is necessary at that instance.

17. Template Matching  Fig. 6. Challenging situations for decision- making

18. Traversal area mapping  Finally with the help of template matching, our system will generate musical tones based on the position of matched template with respect to ‘Safe’ window for the visually impaired person to take either right or left direction.

19. Traversal area mapping  Fig. 7. Safe Window zone marking(left), Commands rendered into sounds(right)

20. Results  The algorithm has generally been robust in predicting the traversability of an area regardless of the image quality, noise and camera vibration.  While testing, most obstacles were accurately detected as non-traversable areas except in situations where they were indistinguishable from the underlying surface.

21. Conclusion  We have described an inexpensive method of identifying assistive devices to develop and build a system for the visually impaired to traverse in safe direction.  Our main concerns are the cost of devices, size of devices, processing time and accuracy.

22.  Thank you for your listening