Common Spatial Patterns for Real-Time Classification of Human Actions


Poppe, Ronald (2010) Common Spatial Patterns for Real-Time Classification of Human Actions. In: Liang Wang & Li Cheng & Guoying Zhao (Eds.), Machine Learning for Human Motion Analysis. IGI Global, Hershey, pp. 55-73. ISBN 9781605669007

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Abstract:We present a discriminative approach to human action recognition. At the heart of our approach is the use of common spatial patterns (CSP), a spatial filter technique that transforms temporal feature data by using differences in variance between two classes. Such a transformation focuses on differences between classes, rather than on modeling each class individually. As a result, to distinguish between two classes, we can use simple distance metrics in the low-dimensional transformed space. The most likely class is found by pairwise evaluation of all discriminant functions, which can be done in real-time. Our image representations are silhouette boundary gradients, spatially binned into cells. We achieve scores of approximately 96% on the Weizmann human action dataset, and show that reasonable results can be obtained when training on only a single subject. We further compare our results with a recent examplar-based approach. Future work is aimed at combining our approach with automatic human detection.
Item Type:Book Section
Electrical Engineering, Mathematics and Computer Science (EEMCS)
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