SUMAN DEB, S. THIRUPATHI REDDY ,UJJWAL BAIDYA, AMIT KUMAR SARKAR, PRATIK RENU, 2012 IEEE

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

Outline Introduction Method

Camera image pre-processing Multi-dimensional makes segmentation Edge Detection Template Matching Traversal area mapping

Results Conclusion

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.

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.

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.

Method

Fig. 1. Traversable area detection methodology.

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.

Camera image pre-processing

Fig. 2. Image analysis into Saturation channel

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 Gi+1 and the corresponding reduced pixel at level Gi.

Multi-dimensional pyramidal segmentation

Fig. 3.Segmentation Results

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).

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).

Edge Detection

Fig. 4. Edge detection after segmentation

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.

Template Matching

Fig. 5. Template Matching

Template Matching Some challenges are there which cannot

be solved by this approach as some rational decision making is necessary at that instance.

Template Matching

Fig. 6. Challenging situations for decision-making

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.

Traversal area mapping

Fig. 7. Safe Window zone marking(left),Commands rendered into sounds(right)

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.

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.

Thank you for your listening