Camera-based On-Road Detections for the Visually Impaired

Abstract

Herein, we research the possibilities of assisting visually impaired pedestrians moving in traffic situations by using camera-based detection of relevant objects in their immediate surroundings. Therefore, we use and adapt algorithms from the field of driver assistance. We present a road background segmentation based on watersheds, whose results are used as input for the presented crosswalk and lane detection algorithms. The crosswalk detection is based on the application of two 1D mean filters and the lane detection on local computations of the EDF (Edge Distribution Function). In our evaluation, the described algorithms achieved good hit rates of 99.87% (road segmentation), 98.64% (crosswalk detection), and 97.89% (lane detection).

Tools, techniques, datasets and application areas for object detection in an image: a review

“…Therefore, the car uses object detection trained models to decide whether to apply break or turn according to identify the nearby objects. Visually impaired persons also use these autonomous driving systems to go anywhere in the world without any dependency on a human being [ 71 ]. Various researchers have developed different data sets such as MIT Pedestrian, INRIA, Catech, KITTI, CityPersons, EuroCity, nuSense, etc., as shown in Table 2 .…”

Pave-Ut—Hungarian environmental load application

“…In this study, direct temperature measurement was performed using a thermometer. Another study presented road background segmentation based on watersheds [6].…”