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Exemplary set of clusters created using the euclidean distance dist euclid. a) and b) show perfect clusters with trajectories of uniform direction in each cluster. c), d) and e) show clusters where two different paths were clustered by mistake. f) shows a cluster of trajectories, where the separation of trajectories according to direction fails, due to the relatively short distance between start and end point of the trajectories. The white circles indicate the starting point of each trajectory.

Exemplary set of clusters created using the euclidean distance dist euclid. a) and b) show perfect clusters with trajectories of uniform direction in each cluster. c), d) and e) show clusters where two different paths were clustered by mistake. f) shows a cluster of trajectories, where the separation of trajectories according to direction fails, due to the relatively short distance between start and end point of the trajectories. The white circles indicate the starting point of each trajectory.

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Article
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We present a novel approach towards the creation of vision based recognition tasks. A lot of domain specific recognition systems have been presented in the past which make use of the large amounts of available video data. The creation of ground truth data sets for the training of theses systems remains difficult and tiresome. We present a system wh...

Contexts in source publication

Context 1
... the metric is suitable for the creation of clusters with uniform direction, it fails if the paths are of varying length. Figure 2 shows an exemplary set of clusters. The paths shown in Figure 2 a) and f) differ significantly in their spatial distances of starting and end points. ...
Context 2
... 2 shows an exemplary set of clusters. The paths shown in Figure 2 a) and f) differ significantly in their spatial distances of starting and end points. Figure 2 c), d) and e) display the major drawback of this distance metric. ...
Context 3
... paths shown in Figure 2 a) and f) differ significantly in their spatial distances of starting and end points. Figure 2 c), d) and e) display the major drawback of this distance metric. Two paths were identified as belonging to one cluster. ...

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