Abstract
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.
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Parrigan, K., Souvenir, R. (2010). Aggregating Low-Level Features for Human Action Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17289-2_14
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DOI: https://doi.org/10.1007/978-3-642-17289-2_14
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