We describe an algorithm for the efficient annotation of events of interest in video microscopy. The specific application involves the detection and tracking of multiple possibly overlapping vesicles in total internal reflection fluorescent microscopy images. A statistical model for the dynamic image data of vesicle configurations allows us to properly weight various hypotheses online. The goal is to find the most likely trajectories given a sequence of images. The computational challenge is addressed by defining a sequence of coarse-to-fine tests, derived from the statistical model, to quickly eliminate most candidate positions at each time frame. The computational load of the tests is initially very low and gradually increases as the false positives become more difficult to eliminate. Only at the last step are state variables estimated from a complete time-dependent model. Processing time thus mainly depends on the number of vesicles in the image and not on image size.