NDLI: Dynamically encoded actions based on spacetime saliency

Content Provider	IEEE Xplore Digital Library
Author	Feichtenhofer, C. Pinz, A. Wildes, R.P.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. Eng. & Comput. Sci., York Univ., Toronto, ON, Canada (Wildes, R.P.) \|\| Inst. of Electr. Meas. & Meas. Signal Process., Tech. Univ. Graz, Graz, Austria (Feichtenhofer, C.; Pinz, A.)
Abstract	Human actions typically occur over a well localized extent in both space and time. Similarly, as typically captured in video, human actions have small spatiotemporal support in image space. This paper capitalizes on these observations by weighting feature pooling for action recognition over those areas within a video where actions are most likely to occur. To enable this operation, we define a novel measure of spacetime saliency. The measure relies on two observations regarding foreground motion of human actors: They typically exhibit motion that contrasts with that of their surrounding region and they are spatially compact. By using the resulting definition of saliency during feature pooling we show that action recognition performance achieves state-of-the-art levels on three widely considered action recognition datasets. Our saliency weighted pooling can be applied to essentially any locally defined features and encodings thereof. Additionally, we demonstrate that inclusion of locally aggregated spatiotemporal energy features, which efficiently result as a by-product of the saliency computation, further boosts performance over reliance on standard action recognition features alone.
Starting Page	2755
Ending Page	2764
File Size	341156
Page Count	10
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7298892
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Motion measurement Energy measurement Spatiotemporal phenomena Encoding Weight measurement Support vector machines Three-dimensional displays
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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