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A camera mounted on an aerial vehicle provides an excellent means for monitoring large areas of a scene. Utilizing several such cameras on different aerial vehicles allows further flexibility, in terms of increased visual scope and in the pursuit of multiple targets. In this paper, we address the problem of associating objects across multiple airborne cameras. Since the cameras are moving and often widely separated, direct appearance-based or proximity-based constraints cannot be used. Instead, we exploit geometric constraints on the relationship between the motion of each object across cameras, to test multiple association hypotheses, without assuming any prior calibration information. Given our scene model, we propose a likelihood function for evaluating a hypothesized association between observations in multiple cameras that is geometrically motivated. Since multiple cameras exist, ensuring coherency in association is an essential requirement, e.g. that transitive closure is maintained between more than two cameras. To ensure such coherency we pose the problem of maximizing the likelihood function as a k-dimensional matching and use an approximation to find the optimal assignment of association. Using the proposed error function, canonical trajectories of each object and optimal estimates of inter-camera transformations (in a maximum likelihood sense) are computed. Finally, we show that as a result of associating objects across the cameras, a concurrent visualization of multiple aerial video streams is possible and that, under special conditions, trajectories interrupted due to occlusion or missing detections can be repaired. Results are shown on a number of real and controlled scenarios with multiple objects observed by multiple cameras, validating our qualitative models, and through simulation quantitative performance is also reported.
Applications, Scene Analysis, Motion, Sensor fusion, Registration

Y. A. Sheikh and M. Shah, "Trajectory Association across Multiple Airborne Cameras," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 361-367, 2007.
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