Multi-object Tracking Based on Particle Probability Hypothesis Density Tracker in Microscopic Video

Research on biological objects requires tracking hundreds of micro-objects from the microscopy video. We propose an automated tracking framework to extract trajectories of micro-objects. This framework uses a particle probability hypothesis density (PF-PHD) tracker to implement a recursive Bayesian state estimation and trajectories association. In the framework, an ellipse target model is presented to describe the micro-objects with shape parameters instead of point-like targets. Furthermore, an orientation and positional constraint model is developed to deal with the data association of crossing trajectories in multitarget tracking. Using this framework, a significantly larger number of tracks are obtained than manual tracking. The experiments on simulated image sequences of microtubule movement are performed in order to evaluate the proposed PF-PHD tracking method.