Abstract
Assistive technologies aim at improving personal mobility of individuals with disabilities, increasing their independence and their access to social life. They include mechanical mobility aids that are increasingly employed amongst the older people who rely on them. However, these devices might fail to prevent falls due to the under-estimation of approaching hazards. Stairs and curbs are among these potential dangers present in urban environments and living accommodations, which increase the risk of an accident. We present and evaluate a low-complexity algorithm to detect descending stairs and curbs of any shape, specifically designed for low-power real-time embedded platforms. Based on a passive stereo camera, as opposed to a 3D active sensor, we assessed the detection accuracy, processing time and power consumption. Our goal being to decide on three possible situations (safe, dangerous and potentially unsafe), we achieve to distinguish more than 94 % dangers from safe scenes within a 91 % overall recognition rate at very low resolution. This is accomplished in real-time with robustness to indoor/outdoor lighting conditions. We show that our method can run for a day on a smartphone battery.
Highlights
IntroductionThe number of mobility impaired people increases especially among the aged individuals
In industrialized countries, the number of mobility impaired people increases especially among the aged individuals
In our previous work [4], we presented promising preliminary results of the evaluation of our stairs detection approach that employs depth information obtained from a stereo vision algorithm based on sum of absolute difference (SAD) methods that are known to be adapted to realtime
Summary
The number of mobility impaired people increases especially among the aged individuals. It includes support to individuals remaining at home, which starts with the access to assistive technologies such as the rollator, a walker equipped with wheels, widely spread among the elderly. These tools can, lead to falls especially in urban zones and buildings. Feature descriptors tend to be robust against orientation and intensity variation while key points are robust to perspective changes. This method can be applied to real-time applications that require a very sparse depth map [3], for example in image registration applications
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