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This paper presents a video-based camera tracker that combines marker-based and feature point-based cues in a particle filter framework. The framework relies on their complementary performance. Marker-based trackers can robustly recover camera position and orientation when a reference (marker) is available, but fail once the reference becomes unavailable. On the other hand, feature point tracking can still provide estimates given a limited number of feature points. However, these tend to drift and usually fail to recover when the reference reappears. Therefore, the authors propose a combination where the estimate of the filter is updated from the individual measurements of each cue. More precisely, the marker-based cue is selected when the marker is available whereas the feature point-based cue is selected otherwise.
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