The rapidly increasing number of resident space objects in Earth’s orbit poses a significant threat to the sustainability of current and future space missions. As Active Debris Removal (ADR) and In-Orbit Servicing (IOS) emerge as critical operations for ensuring the safety and longevity of orbital activities, precise and responsive real-time tracking of the kinematic state of non-cooperative target objects becomes a key enabling technology. In this work, we demonstrate we can improve the performance of an Unscented Kalman Filter (UKF) by exploiting readily available light-curves to extract kinematic priors. Attitude and position estimations from a pose estimation algorithm are fed as the primary input to the UKF, with a sampling rate of up to 1 sample/s in the worst case.