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arXiv · 1509.01947

An end-to-end generative framework for video segmentation and recognition

Hilde Kuehne·Juergen Gall·Thomas Serre
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Abstract

We describe an end-to-end generative approach for the segmentation and recognition of human activities. In this approach, a visual representation based on reduced Fisher Vectors is combined with a structured temporal model for recognition. We show that the statistical properties of Fisher Vectors make them an especially suitable front-end for generative models such as Gaussian mixtures. The system is evaluated for both the recognition of complex activities as well as their parsing into action units. Using a variety of video datasets ranging from human cooking activities to animal behaviors, our experiments demonstrate that the resulting architecture outperforms state-of-the-art approaches for larger datasets, i.e. when sufficient amount of data is available for training structured generative models.

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Hilde Kuehne, Juergen Gall, Thomas Serre. 2016-03-17. An end-to-end generative framework for video segmentation and recognition. https://arxiv.org/abs/1509.01947

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