P-Minder: A Cnn Based Sidewalk Segmentation Approach For Phubber Safety Applications

Obstacle detection on smartphones can support the obstacle avoidance for phubbers, but detecting a driveway phubbing walking is difficult for it. However, driveway and sidewalk with different textures can be divided by semantic segmentation. Therefore, this paper proposes a phubber safety approach, named P-Minder (Phubber Minder), to predict the transition between sidewalk and driveway based on sidewalk semantic segmentation. It determines the range of the sidewalk and driveway respectively by the results of sidewalk segmentation, then reminds the phubbers through their smart phones before they enter the driveway. P-Minder includes a walking detector to provide low-consumption and an adjacent detection to assure the detection accuracy. Additionally, P-Minder can detect the existence of manhole covers or stairs on sidewalk. During the experiment, we establish a sidewalk dataset includes 6 classes: driveway, sidewalk, blind road, stairs, manhole covers and car barrier. The experimental result proves that the segmentation model can reach 77.9% miou. Besides, the method finally achieves 74.19% detection accuracy in the experiment with a low computing resources cost.