Abstract
We present an application of the FuzzyBoost learning algorithm, where the weak learners select spatio-temporal groups of features for waving detection. The features encode the spatial distribution of the optic flow of a tracked person, considering the polar sampling of the flow for each instant. The FuzzyBoost algorithm selects groups of features that discriminate better than any single feature, bringing robustness and generalization over the TemporalBoost algorithm.
This work was supported by FCT (ISR/IST plurianual funding through the PIDDAC Program), partially funded by High Definition Analytics (HDA), QREN - I&D em Co-Promoção 13750 and and by the project CMU-PT/SIA/0023/2009 under the Carnegie Mellon-Portugal Program.
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Moreno, P., Santos-Victor, J. (2013). Waving Detection Using the FuzzyBoost Algorithm and Flow-Based Features. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2013. Lecture Notes in Computer Science, vol 7950. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39094-4_3
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DOI: https://doi.org/10.1007/978-3-642-39094-4_3
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