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In several application areas, such as human computer interaction,surveillance and defence, determining the intent of a tracked object enablessystems to aid the user/operator and facilitate effective, possibly automated,decision making. In this paper, we propose a probabilistic inference approachthat permits the prediction, well in advance, of the intended destination of atracked object and its future trajectory. Within the framework introduced here,the observed partial track of the object is modeled as being part of a Markovbridge terminating at its destination, since the target path, albeit random,must end at the intended endpoint. This captures the underlying long termdependencies in the trajectory, as dictated by the object intent. Bydetermining the likelihood of the partial track being drawn from a particularconstructed bridge, the probability of each of a number of possibledestinations is evaluated. These bridges can also be employed to producerefined estimates of the latent system state (e.g. object position, velocity,etc.), predict its future values (up until reaching the designated endpoint)and estimate the time of arrival. This is shown to lead to a low complexityKalman-filter-based implementation of the inference routine, where any linearGaussian motion model, including the destination reverting ones, can beapplied. Free hand pointing gestures data collected in an instrumented vehicleand synthetic trajectories of a vessel heading towards multiple possibleharbours are utilised to demonstrate the effectiveness of the proposedapproach.

Bayesian Intent Prediction in Object Tracking Using Bridging Distributions