Abstract
We present language-motivated approaches to detecting, localizing and classifying activities and gestures in videos. In order to obtain statistical insight into the underlying patterns of motions in activities, we develop a dynamic, hierarchical Bayesian model which connects low-level visual features in videos with poses, motion patterns and classes of activities. This process is somewhat analogous to the method of detecting topics or categories from documents based on the word content of the documents, except that our documents are dynamic. The proposed generative model harnesses both the temporal ordering power of dynamic Bayesian networks such as hidden Markov models (HMMs) and the automatic clustering power of hierarchical Bayesian models such as the latent Dirichlet allocation (LDA) model. We also introduce a probabilistic framework for detecting and localizing pre-specified activities (or gestures) in a video sequence, analogous to the use of filler models for keyword detection in speech processing. We demonstrate the robustness of our classification model and our spotting framework by recognizing activities in unconstrained real-life video sequences and by spotting gestures via a one-shot-learning approach.
Editors: Isabelle Guyon and Vassilis Athitsos.
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Notes
- 1.
When referring to activity spotting purposes, we use the term gestures instead of activities, only to be consistent with the terminology of the ChaLearn Gesture Challenge.
- 2.
Implementation can be found at http://www.irisa.fr/vista/Equipe/People/Laptev/download.html#stip.
- 3.
States are modeled as multinomials since our input observables are discrete values.
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Acknowledgements
The authors wish to thank the associate editors and anonymous referees for all their advice about the structure, references, experimental illustration and interpretation of this manuscript. The work benefited significantly from our participation in the ChaLearn challenge as well as the accompanying workshops.
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Malgireddy, M.R., Nwogu, I., Govindaraju, V. (2017). Language-Motivated Approaches to Action Recognition. In: Escalera, S., Guyon, I., Athitsos, V. (eds) Gesture Recognition. The Springer Series on Challenges in Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-57021-1_5
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