Indoor staircase detection for supporting security systems in autonomous smart wheelchairs based on deep analysis of the Co-occurrence Matrix and Binary Classification

Abstract

Detecting descending stairs and floors is a crucial aspect of implementing autonomous systems in smart wheelchairs. When the obstacle detection system used in wheelchairs fails to accurately identify descending stairs, it can lead to severe consequences for users, including injuries or, in the worst-case scenario, fatal accidents. Therefore, there is a pressing need for an algorithm that not only exhibits high accuracy in detecting obstacles on descending stairs but also operates with minimal computational delay to ensure an immediate response in wheelchair braking. In this research, We utilize the GLCM technique to extract texture characteristics. Out of these methods, the Decision Tree exhibits the highest accuracy, reaching 94%, with a remarkably fast computational time of 0.01299 s. These promising results were achieved by utilizing the GLCM method with a distance of 2 and an angle of 45°. The accuracy obtained has increased by 2.5% compared to the previous research. Such a level of accuracy, coupled with fast computational performance, enables smart wheelchairs to effectively assist users in identifying obstacles while descending stairs.

Keywords

• Smart wheelchair
• Obstacle detection
• Gray level co-occurrence matrix
• Binary classification