Spatio-temporal constraints for on-line 3D object recognition in videos

This paper considers view-based 3D object recognition in videos. The availability of video sequences allows us to address recognition exploiting both space and time information to build models of the object that are robust to view-point variations. In order to limit the amount of information potentially available in a video we adopt a description of the video content based on the use of local scale-invariant features, both on the object modeling (training sequence) and the recognition phase (test sequences). Then, by means of an ad hoc matching procedure, we look for similar groups of features both in modeling and recognition. The final pipeline we propose is based on the construction of an incremental model of the test sequence, thanks to which we perform on-line recognition. We present experimental results on objects recognition in videos, showing how our approach can be effectively applied to rather complex settings.