Fast and accurate video annotation using dense motion hypotheses

Abstract : Building large video datasets is a crucial task for many applications but is also very expensive in practice. In order to avoid annotating all the frames, the annotations from the labeled frames can be propagated using an offline tracker for each object. Following methods based on dynamic programming and eventually distance transforms, we introduce a new penalization which favors some given displacements between two frames without increasing the complexity of the optimization. In order to speed up this step we also propose to use an exact coarse to fine process. Experimental results show that the proposed energy performs better than previous ones and that our exact coarse to fine optimization leads to a significant speed-up in some scenarios.