On the benefit of state separation for tracking in image space with an interacting multiple model filter
When tracking an object, it is reasonable to assume that the dynamic model can change over time. In practical applications, Interacting Multiple Model (IMM) filter are a popular choice for considering such varying system characteristics. The motion of the object is often modeled using position, velocity, and acceleration. It seems obvious that different image space dimensions can be considered in one overall system state vector. In this paper, the fallacy of simply extending the state vector in case of tracking an object solely in image space is demonstrated. Thereby, we show how under such conditions the effectiveness of an IMM filter can be improved by separating particular states. The proposed approach is evaluated on the VOT 2014 dataset.