We have developed a method to perform the detection and the tracking of microscopic spots directly on four dimensional (3D+t) image data. It extends our previous work by being able to detect with high accuracy multiple biological objects moving in three-dimensional space and by incorporating the possibility to follow moving spots switching between different dynamics characteristics. Our method is based on a two step procedure: first, the objects are detected in the image stacks thanks to a procedure based on a three-dimensional wavelet transform; then the tracking is performed within a Bayesian framework where each object is represented by a state vector evolving according to biologically realistic dynamic models. The main advantage of wavelet-based detection is to be robust to the local variation of contrast and to the imaging noise. The Bayesian tracking allows to predict the new position of a spot knowing its past positions and increases the reliability of the data association step.