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3D Shape Context and Distance Transform for action recognition

10 years 1 months ago
3D Shape Context and Distance Transform for action recognition
We propose the use of 3D (2D+time) Shape Context to recognize the spatial and temporal details inherent in human actions. We represent an action in a video sequence by a 3D point cloud extracted by sampling 2D silhouettes over time. A non-uniform sampling method is introduced that gives preference to fast moving body parts using a Euclidean 3D Distance Transform. Actions are then classified by matching the extracted point clouds. Our proposed approach is based on a global matching and does not require specific training to learn the model. We test the approach thoroughly on two publicly available datasets and compare to several state-ofthe-art methods. The achieved classification accuracy is on par with or superior to the best results reported to date.
Franziska Meier, Irfan A. Essa, Matthias Grundmann
Added 05 Nov 2009
Updated 05 Nov 2009
Type Conference
Year 2008
Where ICPR
Authors Franziska Meier, Irfan A. Essa, Matthias Grundmann
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