In order to quantitatively analyze biological images and study underlying mechanisms of the cellular and subcellular processes, it is often required to track a large number of particles involved in these processes. Manual tracking can be performed by the biologists, but the workload is very heavy. In this paper, we present an automatic particle tracking method for analyzing an essential subcellular process, namely clathrin mediated endocytosis. The framework of the tracking method is an extension of the classical multiple hypothesis tracking (MHT), and it is designed to manage trajectories, solve data association problems, and handle pseudo-splitting/merging events. In the extended MHT framework, particle tracking becomes evaluating two types of hypotheses. The first one is the trajectory-related hypothesis, to test whether a recovered trajectory is correct, and the second one is the observation-related hypothesis, to test whether an observation from an image belongs to a real particle. Here, an observation refers to a detected particle and its feature vector. To detect the particles in 2D fluorescence images taken using total internal reflection microscopy, the images are segmented into regions, and the features of the particles are obtained by fitting Gaussian mixture models into each of the image regions. Specific models are developed according to the properties of the particles. The proposed tracking method is demonstrated on synthetic data under different scenarios and applied to real data.