iPatrol: Illegal Roadside Parking Detection Leveraging On-road Vehicle Crowdsensing

Illegal roadside parking is a common problem faced by large-scale cities, leading to traffic congestion & accidents, and hindering fire rescue. Traditional methods for detecting illegal parking rely highly on active human efforts and particular sensors, which are extremely cost-ineffective to cover large-scale cities. To this end, we consider employing massive on-road vehicles to collect the vehicular sensory data (including recording video of the surroundings and driving state information), thereby enabling a large-scale, fine-grained illegal parking detection at a low cost. However, the dynamic and complex movement of the sensing and target vehicles, coupled with complex traffic situations and environmental factors, presents challenges for achieving accurate detection. To address these challenges, we propose iPatrol, an illegal roadside parking detection system leveraging on-road vehicle crowdsensing, at the heart of which lies a key extension of the Doppler effect from the traditional acoustic scenarios to the vehicle-mounted video scenarios. Following the methodology of the Doppler effect and leveraging camera imaging theory, we establish a new vehicle speed estimation model, using video feature's change to estimate the relative speed of the two vehicles. Furthermore, this model is utilized to identify the parking status of the target vehicle and estimate its position by utilizing the graph rigidity theory and the non-convex optimization scheme. We implement iPatrol on Android smartphones mounted behind the vehicle windshields and conduct on-road experiments covering 233 km roads in an urban area about 125 km(2). The experimental results demonstrate that iPatrol detected a total of 162 illegal parking events while achieving a detection accuracy of 87.1% which outperforms three baselines by 21.9% on average.