variable environment /
mobility, interaction city and crossovers

This paper presents a video-based camera tracker that combines marker-based and feature point-based cues within a particle filter framework. The framework relies on their complementary
performances. On the one hand, 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, filter-based camera trackers using feature point cues can still provide predicted estimates given the previous state. However, the trackers tend to drift and usually fail to recover when the reference reappears. Therefore, we propose a fusion 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 reference is available whereas the feature point-based cue is selected otherwise. Evaluations on real cases show that the fusion of the two approaches outperforms the individual tracking results.