Probabilistic cooperation between trackers of coupled objects

Much work has been done in the field of visual object tracking, yielding a wide range of trackers, including ones aimed for multiple objects. In many cases, there may be a coupling between simultaneously tracked objects, e.g., the locations of some person´s eyes. In such cases, tracking each object independently, or using any multitarget tracker ignoring this coupling, will be suboptimal. This paper addresses these cases and takes advantage of the coupling between the tracked objects to enhance the tracking performance. An analytically justified, probabilistic framework for cooperating between the individual trackers is suggested. The framework is fairly general, allowing to cooperate between any two trackers which output a probability density function of the tracked state, even when the objects are tracked in different state spaces. The framework is successfully tested on two different kinds of trackers, showing the benefit gained from the coupling exploitation.