One faces several challenges when tracking objects and events simultaneously in multi-camera environments — especially if the events associated with the object require precise knowledge of the pose of the object at each instant of time. To illustrate the challenges involved, we consider the problem of tracking bins and their contents at airport security checkpoints. The pose of each bin must be tracked with precision in order to minimize the errors associated with the detection of the various items that the passengers may place in the bins and/or take out of them. Unfortunately, pose estimation of the bins is made difficult by the fact that as a bin moves away from the camera’s optic axis, its appearance changes nonlinearly. This paper presents a collaborative algorithmic framework to address this complexity: The framework includes simple and fast 2D trackers when such tracking can be carried out reliably, and more sophisticated 2.5D trackers when the bins move away from the cameras’ optic axes. The system decides automatically as to which tracker to trust the most. We validate the framework with a ground-truth dataset.