NDLI: Space-Time Robust Representation for Action Recognition

Content Provider	IEEE Xplore Digital Library
Author	Ballas, N. Yi Yang Zhen-Zhong Lan Delezoide, B. Preteux, F. Hauptmann, A.
Copyright Year	2013
Abstract	We address the problem of action recognition in unconstrained videos. We propose a novel content driven pooling that leverages space-time context while being robust toward global space-time transformations. Being robust to such transformations is of primary importance in unconstrained videos where the action localizations can drastically shift between frames. Our pooling identifies regions of interest using video structural cues estimated by different saliency functions. To combine the different structural information, we introduce an iterative structure learning algorithm, WSVM (weighted SVM), that determines the optimal saliency layout of an action model through a sparse regularizer. A new optimization method is proposed to solve the WSVM' highly non-smooth objective function. We evaluate our approach on standard action datasets (KTH, UCF50 and HMDB). Most noticeably, the accuracy of our algorithm reaches 51.8% on the challenging HMDB dataset which outperforms the state-of-the-art of 7.3% relatively.
Sponsorship	IEEE Comput. Soc.
Starting Page	2704
Ending Page	2711
File Size	1194341
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.336
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Support vector machines Context Feature extraction Motion segmentation Encoding Trajectory saliency action recognition pooling WSVM sparse regularization
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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