Dickinson, Patrick and Hunter, Andrew (2006) Body region labelling for action recognition. In: IASTED International Conference on Vizualization, Imaging and Image Processing, 28-30 August 2006, Palma de Mallorca, Spain.
Full text not available from this repository.Abstract
We present a novel method for automatically labelling the head, torso, and legs of a human body tracked through a video sequence. An appearance-based body model is constructed by dividing the initial silhouette into a series of spatial slices, and building a colour distribution histogram for each. In subsequent frames a labelling hypothesis is constructed for each new silhouette by matching against these distributions, and used to identify each body region under a range of poses. We use the body model to extract feature points, which we use as the basis for an action recognition scheme. Actions are represented by a vector of the head and torso positions, sampled over the duration of an action. Manually labelled sequences provide a training set comprising sitting, bending, squatting, and lying actions, viewed from various angles. We use nearest-neighbour matching to identify actions presented in test sequences. Our results show that our method is effective, achieving a high recognition rate.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information: | We present a novel method for automatically labelling the head, torso, and legs of a human body tracked through a video sequence. An appearance-based body model is constructed by dividing the initial silhouette into a series of spatial slices, and building a colour distribution histogram for each. In subsequent frames a labelling hypothesis is constructed for each new silhouette by matching against these distributions, and used to identify each body region under a range of poses. We use the body model to extract feature points, which we use as the basis for an action recognition scheme. Actions are represented by a vector of the head and torso positions, sampled over the duration of an action. Manually labelled sequences provide a training set comprising sitting, bending, squatting, and lying actions, viewed from various angles. We use nearest-neighbour matching to identify actions presented in test sequences. Our results show that our method is effective, achieving a high recognition rate. |
| Keywords: | Action recognition, Body region labelling |
| Subjects: | G Mathematical and Computer Sciences > G400 Computer Science |
| Divisions: | College of Sciences > Faculty of Science > Lincoln School of Computer Science |
| Depositing User: | Patrick Dickinson |
| Date Deposited: | 01 Sep 2006 |
| Last Modified: | 18 Jul 2011 16:11 |
| URI: | http://eprints.lincoln.ac.uk/id/eprint/105 |
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