Towards a general theory of action and time
Artificial Intelligence
Fundamentals of speech recognition
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A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
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A survey on vision-based human action recognition
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Modeling temporal structure of decomposable motion segments for activity classification
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A new pose-based representation for recognizing actions from multiple cameras
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On supervised human activity analysis for structured environments
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Human action recognition by extracting features from negative space
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Human action recognition employing negative space features
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Knives are picked before slices are cut: recognition through activity sequence analysis
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Human action recognition by fast dense trajectories
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Fast action recognition using negative space features
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Advanced Engineering Informatics
Recognizing activities in multiple views with fusion of frame judgments
Image and Vision Computing
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We describe a method of representing human activities that allows a collection of motions to be queried without examples, using a simple and effective query language. Our approach is based on units of activity at segments of the body, that can be composed across space and across the body to produce complex queries. The presence of search units is inferred automatically by tracking the body, lifting the tracks to 3D and comparing to models trained using motion capture data. Our models of short time scale limb behaviour are built using labelled motion capture set. We show results for a large range of queries applied to a collection of complex motion and activity. We compare with discriminative methods applied to tracker data; our method offers significantly improved performance. We show experimental evidence that our method is robust to view direction and is unaffected by some important changes of clothing.