Democratizing Video Human Activity Recognition

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Issue date
2021-07Author
Orellana Trullols, Guillem
Suggested citation
Orellana Trullols, Guillem;
.
(2021)
.
Democratizing Video Human Activity Recognition.
http://hdl.handle.net/10459.1/83154.
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Show full item recordAbstract
The exponential growth of video sources available like
smartphones, surveillance video cameras and video sharing
platforms, and the recent advances related to video encoding,
storage, and computational resources, has engaged
researchers to further explore the computer's capability of
understanding videos.
This thesis establishes a deep learning framework based on
good practices wrapping the human activity recognition in
videos field. The framework emphasizes the reproducibility
of experiments and encourages the use of techniques to
maximize the learning capabilities of video understanding
models.
Our main contributions are open sourcing an Activity
Recognition (AR) Python package and the creation of a
reduced dataset called SARD alongside an ablation study
demonstrating that having access to a small number of
annotated videos is not a limitation to obtain a robust video
classifier.
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