This paper presents a multi-camera system to track multiple persons in complex, dynamic environments. Position measurements are obtained by carving out the space defined by foreground regions in the overlapping camera views and projecting these onto blobs on the ground plane. Person appearance is described in terms of the colour histograms in the various camera views of three vertical body regions (head-shoulder, torso, legs). The assignment of measurements to tracks (modelled by Kalman filters) is done in a non-greedy, global fashion based on ground plane position and colour appearance. The advantage of the proposed approach is that the decision on correspondences across cameras is delayed until it can be performed at the object-level, where it is more robust. We demonstrate the effectiveness of the proposed approach using data from three cameras overlooking a complex outdoor setting (train platform), containing a significant amount of lighting and background changes.