Instant Discovery of Moment Companion Vehicles from Big Streaming Traffic Data

Abstract

With more and more traffic monitoring cameras installed in large cities, big streaming data are being generated continuously and provides a lot of new application opportunities. Among the new applications, companion vehicle discovery is to identify vehicle groups that move together. To quickly identify companion vehicles from a special type of streaming traffic data, called Automatic Number Plate Recognition (ANPR) data, this paper proposes a framework and two algorithms following distributed data-parallel programming model. The main challenge is how to handle the scale of ANPR data and detect companion vehicles as soon as possible. The proposed framework is designed to instantly output companion vehicles when they pass through monitoring cameras. Our framework can be used in many time-sensitive scenarios like surveillance on suspect trackers for specific vehicles. Experiments with real ANPR data in a distributed environment verify that our approach can process streaming ANPR data directly and discover the companion vehicles in nearly real time. We also analyze the performance affecting factors from the experiments.