Combined Motion and Appearance Models for Robust Object Tracking in Real-Time

Abstract

This paper proposes a tracking architecture that finds a trade-off between accuracy and efficiency, via a combined solution of motion and appearance information. We explore the use of color features into a tracking pipeline based on Kalman filtering. The devised architecture is made of simple modules, combined to reach a robust final result, while keeping the computation cost low (we perform 20 fps). The method has been evaluated on three benchmark datasets and is currently under use on real video-surveillance systems, reporting very good tracking results.