Fast and accurate video annotation using dense motion hypotheses - CEA - Commissariat à l’énergie atomique et aux énergies alternatives Access content directly
Conference Papers Year : 2014

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.
Not file

Dates and versions

cea-01841687 , version 1 (17-07-2018)

Identifiers

Cite

L. Fagot-Bouquet, J. Rabarisoa, Q.C. Pham. Fast and accurate video annotation using dense motion hypotheses. 2014 IEEE International Conference on Image Processing (ICIP), Oct 2014, Paris, France. pp.3122-3126, ⟨10.1109/ICIP.2014.7025631⟩. ⟨cea-01841687⟩

Collections

CEA DRT LIST
22 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More