When establishing object correspondence across non-overlapping cameras, the existing methods combine separate likelihoods of appearance and kinematic features in a Bayesian framework, constructing a joint likelihood to compute the probability of re-detection. A drawback of these methods is not having a proper approach to reduce the search space when localizing an object in a subsequent camera once the kinematic and appearance features are extracted in the current camera. In this work we introduce a novel methodology to condition the location of an object on its appearance and time, without assuming independence between appearance and kinematic features, in contrast to existing work. We characterize the linear movement of objects in the unobserved region with an additive Gaussian noise model. Assuming that the cameras are affine, we transform the noise model onto the image plane of subsequent cameras. We have tested our method with toy car experiments and real-world camera setups and found that the proposed noise model acts as a prior to improving re-detection. It constrains the search space in a subsequent camera, greatly improving the computational efficiency. Our method also has the potential to distinguish between objects similar in appearance, and recover correct labels when they move across cameras.
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