We introduce a novel and robust method for multi-object tracking from multiple uncalibrated cameras. This method improves consistent labeling by incorporating the field of view lines and location information exchange between cameras by using the projective invariants in P 2. Each camera keeps its own tracks for each target object. This provides improved tracking as well as distributed processing, in which each camera is operated by a separate CPU that performs its own tracking and labeling. The tracking in each camera view is performed by using a two-level hierarchical structure. The main novelties of the proposed method include: a) the ability to communicate between the cameras at any time to improve and update the tracks of an object instead of tracking in each view independently, and to perform this without camera calibration; b) updating the track of an object without interruption and without any need for an estimation of the moving speed and direction, even if the object is totally invisible. The proposed method recovered 90% of the full occlusion cases. The hierarchical tracking structure makes the algorithm computationally efficient and, after background elimination, the first-level tracking runs at about 62 fps on a 2GHz Celeron machine without code optimization. We present results obtained from the PETS2001 database, which show the success of the camera communication in partial and complete occlusions.