Coaction discovery: segmentation of common actions across multiple videos
Proceedings of the Twelfth International Workshop on Multimedia Data Mining
Modeling complex temporal composition of actionlets for activity prediction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Atomic action features: a new feature for action recognition
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Recognizing complex events using large margin joint low-level event model
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Auto learning temporal atomic actions for activity classification
Pattern Recognition
Warped K-Means: An algorithm to cluster sequentially-distributed data
Information Sciences: an International Journal
Sparse representation for recognizing object-to-object actions under occlusions
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Coloring Action Recognition in Still Images
International Journal of Computer Vision
Activity representation with motion hierarchies
International Journal of Computer Vision
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We address the problem of detecting actions, such as drinking or opening a door, in hours of challenging video data. We propose a model based on a sequence of atomic action units, termed "actoms", that are characteristic for the action. Our model represents the temporal structure of actions as a sequence of histograms of actom-anchored visual features. Our representation, which can be seen as a temporally structured extension of the bag-of-features, is flexible, sparse and discriminative. We refer to our model as Actom Sequence Model (ASM). Training requires the annotation of actoms for action clips. At test time, actoms are detected automatically, based on a non parametric model of the distribution of actoms, which also acts as a prior on an action's temporal structure. We present experimental results on two recent benchmarks for temporal action detection, "Coffee and Cigarettes" and the dataset of. We show that our ASM method outperforms the current state of the art in temporal action detection.