An algorithm for feature point tracking is proposed. The Interacting Multiple Model (IMM) filter is used to estimate the
state of a feature point. The problem of data association, i.e. establishing which feature point to use in the state estimator,
is solved by an assignment algorithm. A track management method is also developed. In particular a track continuation
method and a track quality indicator are presented. The evaluation of the tracking system on real sequences shows that the
IMM filter combined with the assignment algorithm outperforms the Kalman filter, used with the Nearest Neighbour (NN)
filter, in terms of data association performance and robustness to sudden feature point manoeuvre.
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. The particularity of the fusion filter is to manipulate different sorts of cues in a single framework. The framework keeps a single motion model and its prediction is corrected by one cue at a time. More precisely, the marker-based cue is selected when the reference is available whereas the feature point-based cue is selected otherwise. The filter's state is updated by switching between two different likelihood distributions. Each likelihood distribution is adapted to the type of measurement (cue). Evaluations on real cases show that the fusion of these two approaches outperforms the individual tracking results.