Find you if you drive: Inferring home locations for vehicles with surveillance camera data

Abstract

Inferring home locations for users from spatiotemporal data has become increasingly important for real-world applications ranging from security, recommendation, advertisement targeting, to transportation scheduling. Existing home location inference studies are based either on geo-tagged social media data or continuous GPS data. Yet this inference problem in highly sparse vehicle trajectories in urban surveillance systems remains largely unexplored. In this paper, we propose an accurate home location inference framework for vehicles in urban traffic surveillance systems by considering both spatial and temporal characteristics. To the best of our knowledge, we are the first to predict exact home community for vehicles at such a fine granularity using the sparse and noisy surveillance camera data. First, we collect and preprocess multiple contextual datasets to obtain a context-rich road network with residential communities and surveillance cameras. Second, we detect the potential home location areas for each vehicle by clustering Origin–Destination (O-D) pairs extracted in vehicle’s camera-based trajectories. Then we further propose an in∕out time pattern to distinguish the home area candidate from the O-D clusters by leveraging time-aware constraints. Furthermore, to find the exact home community, we propose a Kernel Density Estimation (KDE) based inference method with a local camera selection strategy to effectively identify the home community from the residential communities near/in the home area candidate. Our comprehensive experiments on a large-scale real-world dataset demonstrate the effectiveness of our proposed method.