ThaiScience  


ENGINEERING AND APPLIED SCIENCE RESEARCH


Volume 48, No. 05, Month SEPTEMBER, Year 2021, Pages 593 - 603


Fast obstacle detection system for the blind using depth image and machine learning

Surapol Vorapatratorn, Atiwong Suchato, Proadpran Punyabukkana


Abstract Download PDF

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



ENGINEERING AND APPLIED SCIENCE RESEARCH


Published by : Faculty of Engineering, Khon Kaen University
Contributions welcome at : https://ph01.tci-thaijo.org/index.php/easr/index