An object-tracking algorithm based on particle filtering with region-based level set method

In this paper, to realize robust tracking, we propose a particle filter (PF) model to track a single paramecium. The proposed PF model consists of a system dynamical model and an observation model. The information about our tracking object is described by a state vector and the system state is assumed to evolve according to the system dynamical model. The parallel region-based level set method with displacement correction (PR-LSM-DC) proposed in our previous work now works as the measurements for the PF model. The tracking is achieved by estimating the state of a moving object from the observations. Experiments show that with motion prediction using the PF model, we increase the robustness of tracking and extend the duration of single paramecium tracking. The 2 [ms] computational time indicates that we developed an algorithm and a computer aided system which achieves nonrigid single micro-organisms tracking in real-time as they deform, move and collide with others under optical microscope.