Improvements for pedestrian safety application P-Minder

Abstract

Abstract P-Minder is a lightweight sidewalk segmentation and obstacle detection-based approach designed for phubbers’ safety, which can help phubbers to avoid obstacles and other dangers during phubbing walking. But two issues have been found in previous experiments: P-Minder undifferentiated scores the recognition results and costs high resource occupation during background operation. Therefore, this paper proposes the sector correlation model to classify the recognition and segmentation results for an accurate judgment. Then, the new application architecture design and walking detection model decrease the resource occupation. Besides, the datasets are expanded, and the models are retrained to adapt to the improved architecture. In the same datasets, P-Minder achieves a 1.7 percentage point accuracy improvement during the experiment. The experimental result proves that the segmentation model can reach 81.2% mean intersection over union. Besides, the method finally achieves 75.52% detection accuracy in the experiment with a low computing resources cost.

Keywords

• Sidewalk dataset
• Sidewalk segmentation
• Obstacles detection
• Phubber safety
• Reliable inspection