Incremental object learning and robust tracking of multiple objects from RGB-D point set data
A novel approach for tracking multiple objects from RGB-D point set data. The incremental learning method allows tracking objects without prior knowledge. The robustness of the method was quantitatively analyzed in the interaction cases. The trade-off between the efficiency and accuracy was empirically examined. The limitations of the novel method were investigated in various moving objects.
rgb-d point set data incremental learning multiple objects tracking gaussian mixture models 3-d point set registration robot vision visual tracking tempo-spatial data association