Assistive technologies aim at improving personal mobility of individuals with disabilities, increasing their\nindependence and their access to social life. They include mechanical mobility aids that are increasingly employed\namongst the older people who rely on them. However, these devices might fail to prevent falls due to the\nunder-estimation of approaching hazards. Stairs and curbs are among these potential dangers present in urban\nenvironments and living accommodations, which increase the risk of an accident. We present and evaluate a\nlow-complexity algorithm to detect descending stairs and curbs of any shape, specifically designed for low-power\nreal-time embedded platforms. Based on a passive stereo camera, as opposed to a 3D active sensor, we assessed the\ndetection accuracy, processing time and power consumption. Our goal being to decide on three possible situations\n(safe, dangerous and potentially unsafe), we achieve to distinguish more than 94 % dangers from safe scenes within a\n91 % overall recognition rate at very low resolution. This is accomplished in real-time with robustness to\nindoor/outdoor lighting conditions. We show that our method can run for a day on a smartphone battery.
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