“Particle Filter based on Dual Model for Irregular Moving Object Tracking”

Particle filter is well known as a robust object tracking algorithm based on prediction with many distributed particles and is widely used for many practical applications. However, since conventional methods use one state transition model, the tracking accuracy is decreased for the objects with irregular motion. This paper proposes a dual model particle filter based on two state transition models which targets for irregular moving object tracking. By using two state transition models which have different properties each of them, the proposed method makes it possible to track stably even if the object suddenly change its direction. Evaluation results with a software simulation shows that the proposed method attains high tracking accuracy for a irregular moving scene, for example bounding ball on floor or wall, compared with conventional ones.