Our research proposes a novel obstacle detection and navigation system for the blind using stereo cameras with machine learning
techniques. The obstacle classification result will navigate users through a difference directional sound patterns via bone conductive
stereo headphones. In the first stage, the Semi-Global block-matching technique was used to transform stereo images to depth image
which can be used to identify the depth level of each image pixel. Next, fast 2D-based ground plane estimation which was separate
obstacle image from the depth image with our Horizontal Depth Accumulative Information (H-DAI). The obstacle image will be then
converted to our Vertical Depth Accumulative Information (V-DAI) which was extracted by a feature vector to train the obstacle
model. Our dataset consists of 34,325 stereo-gray images in 7 different obstacle class. Our experiment compared various machine
learning algorithms (ANN, SVM, Naïve Bayes, Decision Tree, k-NN and Deep Learning) performance between classification accuracy
and prediction speed. The results show that using ANN with our H-DAI and V-DAI reaches 96.45% in obstacle classification accuracy
and 23.76 images per second for processing time which is 6.75 times faster than the recently ground plane estimate technique.
Keywords
Computer vision, Scene understanding, Machine learning, Assistive technology, Visually impaired