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The authors present a novel multi-camera multi-target fusion and tracking algorithm for noisy data. Information fusion is an important step towards robust multi-camera tracking and allows them to reduce the effect of projection and parallax errors as well as of the sensor noise. Input data from each camera view are projected on a top-view through multi-level homographic transformations. These projected planes are then collapsed onto the top-view to generate a detection volume. To increase track consistency with the generated noisy data they propose to use a Track-Before-Detect Particle Filter (TBD-PF) on a 5D state-space. TBD-PF is a Bayesian method which extends the target state with the signal intensity and evaluates each image segment against the motion model.
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