Multi-object tracking is a major area of research because of its wide application scope. In this paper we describe a set of improvements, toward video surveillance context, to the multi-object tracker proposed by Di Lascio (CVIU 2013). First, we generalize the tracking by removing the specialization made for pedestrians. Then, we integrate easily available scene knowledge in order to allow three-dimensional reasoning and better handle occlusions. Additionally, we improve the group creation and destruction mechanism by adding an association pass and an overlap similarity criterion. We evaluate the proposed method on several synthetic and real-world videos.