The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Behavior Recognition via Sparse Spatio-Temporal Features
Abstract
A common trend in object recognition is to detect and leverage the use of sparse, informative feature points. The use of such features makes the problem more manageable while providing increased robustness to noise and pose variation. In this work we develop an extension of these ideas to the spatio-temporal case. For this purpose, we show that the direct 3D counterparts to commonly used 2D interest point detectors are inadequate, and we propose an alternative. Anchoring off of these interest points, we devise a recognition algorithm based on spatio-temporally windowed data. We present recognition results on a variety of datasets including both human and rodent behavior.
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Cites
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Learning Spatiotemporal Features with 3D Convolutional Networks
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SlowFast Networks for Video Recognition
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Metadata
- Authors
- Dollar, P., Rabaud, V., Cottrell, G., & Belongie, S.
- Venue
- 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance
- Publisher
- IEEE
- Published
- 2006-01-18
- Pages
- 65-72
- OpenAlex
- W2533739470
- Cited by
- 2,460
- References
- 5
- FWCI
- 68.71
- Type
- conference paper
- Language
- en
- Open access
- closed
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