SpeedEye: A Crowdsourced Speeding Detection System for Safer Urban Neighborhoods

Speeding remains a significant road safety issue in cities across Michigan, as cited by City Police Departments and resident feedback. Despite substantial efforts, Michigan's prohibition of speed detection cameras limits the effectiveness of existing measures, leaving residents vulnerable to the dangers of speeding. To address this challenge, this paper introduces SpeedEye, a novel crowdsourcing-based speeding detection system designed to empower residents to automatically capture and report speeding incidents. By repurposing outdated smartphones no longer in use, the system harnesses their cameras, transmission capabilities, and computational power to deliver a cost-effective, community-driven solution for traffic safety. Utilizing advanced computer vision techniques, such as perspective transformation and background subtraction, SpeedEye ensures accuracy under diverse conditions, including varying deployment locations and vehicle types. Real-world evaluations demonstrate the system's reliability and effectiveness in detecting vehicle speeds, highlighting its potential to significantly enhance road safety. By pairing community involvement with innovative technology, this solution offers a scalable and practical approach to mitigating speeding in urban neighborhoods.