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Spot On: Action Localization from Pointly-Supervised Proposals

  • Pascal MettesEmail author
  • Jan C. van Gemert
  • Cees G. M. Snoek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9909)

Abstract

We strive for spatio-temporal localization of actions in videos. The state-of-the-art relies on action proposals at test time and selects the best one with a classifier trained on carefully annotated box annotations. Annotating action boxes in video is cumbersome, tedious, and error prone. Rather than annotating boxes, we propose to annotate actions in video with points on a sparse subset of frames only. We introduce an overlap measure between action proposals and points and incorporate them all into the objective of a non-convex Multiple Instance Learning optimization. Experimental evaluation on the UCF Sports and UCF 101 datasets shows that (i) spatio-temporal proposals can be used to train classifiers while retaining the localization performance, (ii) point annotations yield results comparable to box annotations while being significantly faster to annotate, (iii) with a minimum amount of supervision our approach is competitive to the state-of-the-art. Finally, we introduce spatio-temporal action annotations on the train and test videos of Hollywood2, resulting in Hollywood2Tubes, available at http://tinyurl.com/hollywood2tubes.

Keywords

Action localization Action proposals 

Notes

Acknowledgements

This research is supported by the STW STORY project.

Supplementary material

419978_1_En_27_MOESM1_ESM.pdf (870 kb)
Supplementary material 1 (pdf 870 KB)

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Pascal Mettes
    • 1
    Email author
  • Jan C. van Gemert
    • 2
  • Cees G. M. Snoek
    • 1
  1. 1.University of AmsterdamAmsterdamNetherlands
  2. 2.Delft University of TechnologyDelftNetherlands

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