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This paper introduces MEXSVMs, a mid-level representation enabling
efficient recognition of actions in videos. The entries in our
descriptor are the outputs of several movement classifiers evaluated
over spatial-temporal volumes of the image sequence, using space-time
interest points as low-level features. Each movement classifier is a
simple exemplar-SVM, i.e., an SVM trained using a single positive
video and a large number of negative sequences.
Our representation offers two main advantages. First, since our
mid-level features are learned from individual video exemplars, they
require minimal amount of supervision. Second, we show that even
simple linear classification models trained on our global video
descriptor yield action recognition accuracy comparable to the
state-of-the-art. Because of the simplicity of linear models, our
descriptor can efficiently learn classifiers for a large number of
different actions and to recognize actions even in large video
databases. Experiments on two of the most challenging action
recognition benchmarks demonstrate that our approach achieves accuracy
similar to the best known methods while performing 70 times faster
than the closest competitor.
Bibliographic citation for this report: [plain text] [BIB] [BibTeX] [Refer]
Or copy and paste:
Du Tran and Lorenzo Torresani, "MEXSVMs: Mid-level Features for Scalable Action Recognition." Dartmouth Computer Science Technical Report TR2013-726, January 2013.
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