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Published in:
2010 | OriginalPaper | Chapter
Aggregating Low-Level Features for Human Action Recognition
Authors : Kyle Parrigan, Richard Souvenir
Published in: Advances in Visual Computing
Publisher: Springer Berlin Heidelberg
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Recent methods for human action recognition have been effective using increasingly complex, computationally-intensive models and algorithms. There has been growing interest in automated video analysis techniques which can be deployed onto resource-constrained distributed smart camera networks. In this paper, we introduce a multi-stage method for recognizing human actions (e.g., kicking, sitting, waving) that uses the motion patterns of easy-to-compute, low-level image features. Our method is designed for use on resource-constrained devices and can be optimized for real-time performance. In single-view and multi-view experiments, our method achieves 78% and 84% accuracy, respectively, on a publicly available data set.