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This paper presents a new approach to the problem of camera localization with non-overlapping camera views, particularly relevant for video surveillance systems. We show how to recast localization as quasi-convex optimization under the L-Infinity norm. Thereby we add the problem of reconstructing camera centers and 3D points for non-overlapping cameras with known internal parameters and known rotations...
This paper proposes a method that localizes two surveillance cameras and simultaneously reconstructs object trajectories in 3D space. The method is an extension of the Direct Reference Plane method, which formulates the localization and the reconstruction as a system of linear equations that is globally solvable by Singular Value Decomposition. The method's assumptions are static synchronized cameras,...
Tracking across non-overlapping camera views is still an unsolved problem. Appearance is a popular cue that does not work when the views are considerably different. This paper proposes tracking in the 3-D space without using appearance. We propose to use the geometry between the two cameras. Linear inhomogeneous triangulation is expanded by a Gaussian random walk model. Tracking is then triangulation...
This paper proposes a method to compute the fundamental matrix from slightly overlapping views. Slight overlaps are preferable to substantial overlaps in large multi-camera surveillance applications, because otherwise the number of cameras and thus the overall application costs would gravely increase. Instead of using point correspondences alone, we additionally use the infinite homography. The infinite...
We propose a novel algorithm to find highly frequented paths of motion trajectories obtained from video sequences. This is achieved by representing the motion trajectories in the scene as sequences of prototypes obtained by a combined vector quantization and growing neural gas algorithm. In contrast to existing methods, the proposed algorithm can be applied to data sets containing motion trajectories...
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