This paper is placed in the context of video traffic analysis. Typically, hundreds of cameras installed in cities produce a very large amount of data, impossible to handle without automatic processing. For helping traffic controllers to take their decisions, it is important to know in real time, the state of the traffic (number and speed of vehicles on each track segment) but also to have temporal statistics of these measures throughout the day, the week or the year. We can obtain such information by detection and tracking of moving objects in videos, which is a widely studied domain. Nevertheless, most of the automatic video analysis systems face many difficulties: occlusions, variation of the apparent size of objects, illumination changes, etc. In these difficult cases, traditional methods provide only partial trajectories of objects. Rather than trying to make these systems more robust for individual tracking, we propose to aggregate the partial data to build global information on the flow of vehicles in the scene. Specifically, we propose a method which, at first, automatically identifies input E_i and output X_j areas in the scene. Secondly, for each pair E_i, X_j, we record the number n_i,j of vehicles that enter the scene in E_i and leave it in X_j.