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The study of cell and pathogen motility in biology requires computerized methods to enable objective quantitative analysis of large amounts of data. In this paper, we propose a method to detect and track multiple moving biological objects showing different kind of dynamics in image sequences acquired through fluorescence video microscopy. It enables the extraction and analysis of informations such as number, position, speed and movement phases of, e.g., endosomes and viral particles. The method consists of four stages. After a detection stage performed by an undecimated wavelet transform, we compute, for each detected spot, several predictions of its future state in the next frame. This is accomplished thanks to an interacting multiple model (IMM) algorithm which includes several models corresponding to different movement types. Tracks are constructed thereafter by a data association algorithm based on the maximization of the estimated likelihood of each IMM. The last stage consists in updating the IMM filters in order to compute final estimations for the present image and to improve predictions for the next image. The performance of the method is illustrated on synthetic image data.