In this paper, we propose a vision tracking system primarily targeted for systems with low computing resources. It is based on GMPHD filter and can deal with occlusion between objects. The proposed algorithm is supposed to work in a node of camera network where the cost of the computer processing the information is critical. To achieve a low computing complexity, a basic background subtraction algorithm combined with a connected component analysis method are used to detect the objects of interest. GMPHD was improved to detect occlusions between objects and to handle their identities once the occlusion ends. The occlusion is detected using a low complexity distance criterion that takes into consideration the object's bounding box. When an occlusion is noticed, the features of the overlapped objects are saved. At the end of the overlapping, the extracted features are compared to the current features of the objects to perform the object reidentification. In our experiments two different features are tested: color histogram features and motion features. The experiments are performed on two datasets: PETS2009 and CAVIAR. The obtained results show that our approach ensures a high improvement of GMPHD filter and has a low computing complexity.