Acoustic-based Object Detection for Pedestrian Using Smartphone
Zi Wang, Jie Yang

TL;DR
ObstacleWatch is a smartphone-based acoustic system that detects obstacles in front of pedestrians using inaudible signals, achieving over 92% accuracy and low energy consumption to enhance safety for smartphone users while walking.
Contribution
This paper introduces ObstacleWatch, a novel acoustic obstacle detection system utilizing inaudible signals and stereo microphones to accurately estimate obstacle distance, size, and angle.
Findings
Achieves over 92% obstacle collision prediction accuracy
Estimates obstacle distance with about 2 cm error
Works across different phone models and obstacle types
Abstract
Walking while using a smartphone is becoming a major pedestrian safety concern as people may unknowingly bump into various obstacles that could lead to severe injuries. In this paper, we propose ObstacleWatch, an acoustic-based obstacle collision detection system to improve the safety of pedestrians who are engaged in smartphone usage while walking. ObstacleWatch leverages the advanced audio hardware of the smartphone to sense the surrounding obstacles and infers fine-grained information about the frontal obstacle for collision detection. In particular, our system emits well-designed inaudible beep signals from the smartphone built-in speaker and listens to the reflections with the stereo recording of the smartphone. By analyzing the reflected signals received at two microphones, ObstacleWatch is able to extract fine-grained information of the frontal obstacle including the distance,…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Video Surveillance and Tracking Methods · Human Mobility and Location-Based Analysis
