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Erschienen in:
2014 | OriginalPaper | Buchkapitel
sNN-LDS: Spatio-temporal Non-negative Sparse Coding for Human Action Recognition
verfasst von : Thomas Guthier, Adrian Šošić, Volker Willert, Julian Eggert
Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2014
Verlag: Springer International Publishing
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Current state-of-the-art approaches for visual human action recognition focus on complex local spatio-temporal descriptors, while the spatio-temporal relations between the descriptors are discarded. These bag-of-features (BOF) based methods come with the disadvantage of limited descriptive power, because class-specific mid- and large-scale spatio-temporal information, such as body pose sequences, cannot be represented. To overcome this restriction, we propose sparse non-negative linear dynamical systems (sNN-LDS) as a dynamic, parts-based, spatio-temporal representation of local descriptors. We provide novel learning rules based on sparse non-negative matrix factorization (sNMF) to simultaneously learn both the parts as well as their transitions. On the challenging UCF-Sports dataset our sNN-LDS combined with simple local features is competitive with state-of-the-art BOF-SVM methods.