Vehicle Detection and Tracking in Wide Field-of-View Aerial Video

Abstract

This paper presents a joint probabilistic relation graph approach to simultaneously detect and track a large number of vehicles in low frame rate aerial videos. Due to low frame rate, low spatial resolution and sheer number of moving objects, detection and tracking in wide area video poses unique challenges. In this paper, we explore vehicle behavior model from road structure and generate a set of constraints to regulate both object based vertex matching and pairwise edge matching schemes. The proposed relation graph approach then unifies these two matching schemes into a single cost minimization framework to produce a quadratic optimized association result. The experiments on hours of real videos demonstrate the graph matching framework with vehicle behavior model effectively improves tracking performance in large scale dense traffic scenarios.