Dynamical Gaussian mixture model for tracking elliptical living objects
In this letter, we present a novel dynamical Gaussian mixture model (DGMM) for tracking elliptical living objects in video frames. The parameters, which inform object position and shape, are estimated by using a traditional Gaussian mixture model (GMM) for the first frame. Instead of simply using the parameters of one frame as the initial state for the next frame, DGMM employs Kalman filter to predict these parameters to initialize the evolution of GMM for the rest of sequel frames. Compared with the simple way, the estimate by Kalman filter is usually much closer to the true state, the computation load for tracking is reduced significantly. In addition, split, merge and delete operations are integrated into DGMM to account for object splitting, merging and vanishing. This scheme can suppress the chance of getting stuck at a local maximum, led by a mismatch between the number of objects in the model and the actual number of objects. Results obtained from experiments on both simulated and real-world data demonstrate the effectiveness of our algorithm. DGMM has been successfully applied to track the early development process of urchin embryo in which the events of motility and cleavage occur.
gaussian mixture model tracking kalman filtering em algorithm