Multiple particle tracking (MPT) has seen numerous applications in live-cell imaging studies of subcellular dynamics. Establishing correspondence between particles in a sequence of frames with high particle density, particles merging and splitting, particles entering and exiting the frame, temporary particle disappearance, and an ill-performing detection algorithm is the most challenging part of MPT. Here we propose a tracking method based on multidimensional assignment to address these problems. We combine an Interacting Multiple Model (IMM) filter, multidimensional assignment, particle occlusion handling, and merge-split event detection in a single software analysis package. The main advantage of a multidimensional assignment is that both spatial and temporal information can be used by using several later frames as reference. The IMM filter, which is used to maintain and predict the state of each track, contains several models which correspond to different types of biologically realistic movements. It works especially well with multidimensional assignment, because there tends to be a higher probability of correct particle association over time. First the method generates many particle-correspondence hypotheses, merge-split hypotheses and misdetection hypotheses within the framework of a sliding window over the frames of the image sequence. Then it builds a multidimensional assignment problem (MAP) accordingly. The particle is tracked with gap-filling, and merging and splitting events are then detected using the MAP solution. The tracking method is validated on both simulated tracks and microscopy image sequences. The results of these experiments show that the method is more accurate and robust than other "tracking from detected features" methods in dense particle situations. (C) 2010 Elsevier Inc. All rights reserved.