'Important stuff, everywhere!' Activity recognition with salient proto-objects as context
| Autor: | Boris Schauerte, Lukas Rybok, Rainer Stiefelhagen, Ziad Al-Halah |
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| Rok vydání: | 2014 |
| Předmět: |
Computer science
business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Context (language use) Image segmentation Machine learning computer.software_genre Object (computer science) Motion (physics) Activity recognition Salient Artificial intelligence business computer Meaning (linguistics) Simple (philosophy) |
| Zdroj: | WACV |
| Popis: | Object information is an important cue to discriminate between activities that draw part of their meaning from context. Most of current work either ignores this information or relies on specific object detectors. However, such object detectors require a significant amount of training data and complicate the transfer of the action recognition framework to novel domains with different objects and object-action relationships. Motivated by recent advances in saliency detection, we propose to employ salient proto-objects for unsupervised discovery of object- and object-part candidates and use them as a contextual cue for activity recognition. Our experimental evaluation on three publicly available data sets shows that the integration of proto-objects and simple motion features substantially improves recognition performance, outperforming the state-of-the-art. |
| Databáze: | OpenAIRE |
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