Acoustic-based Object Detection for Pedestrian Using Smartphone

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, angle, and size for detecting the possible collisions and to alert users. Our experimental evaluation under two real-world environments with different types of phones and obstacles shows that ObstacleWatch achieves over 92% accuracy in predicting obstacle collisions with distance estimation errors at about 2 cm. Results also show that ObstacleWatch is robust to different sizes of objects and is compatible with different phone models with low energy consumption.