3D Shape Context and Distance Transform for action recognition
Grundmann, M.
Meier, F.
Essa, I.
Georgia Inst. of Technol., Atlanta, GA;
This paper appears in: Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Publication Date: 8-11 Dec. 2008
On page(s): 1-4
Location: Tampa, FL,
ISSN: 1051-4651
ISBN: 978-1-4244-2174-9
INSPEC Accession Number: 10457779
Digital Object Identifier: 10.1109/ICPR.2008.4761435
Current Version Published: 2009-01-23
Abstract
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-of-the-art methods. The achieved classification accuracy is on par with or superior to the best results reported to date.
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