Unsupervised Deep Networks for Temporal Localization of Human Actions in Streaming Videos
| Autor: | Binu M. Nair |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Structure (mathematical logic)
Restricted Boltzmann machine Artificial neural network business.industry Computer science 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Class (biology) Motion (physics) Action (philosophy) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Layer (object-oriented design) business computer Generative grammar 0105 earth and related environmental sciences |
| Zdroj: | Advances in Visual Computing ISBN: 9783319508313 ISVC (2) |
| DOI: | 10.1007/978-3-319-50832-0_15 |
| Popis: | We propose a deep neural network which captures latent temporal features suitable for localizing actions temporally in streaming videos. This network uses unsupervised generative models containing autoencoders and conditional restricted Boltzmann machines to model temporal structure present in an action. Human motions are non-linear in nature, and thus require continuous temporal model representation of motion which are crucial for streaming videos. The generative ability would help predict features at future time steps which can give an indication of completion of action at any instant. To accumulate M classes of action, we train an autencoder to seperate out actions spaces, and learn generative models per action space. The final layer accumulates statistics from each model, and estimates action class and percentage of completion in a segment of frames. Experimental results prove that this network provides a good predictive and recognition capability required for action localization in streaming videos. |
| Databáze: | OpenAIRE |
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