The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Action snippets: How many frames does human action recognition require?
Abstract
Visual recognition of human actions in video clips has been an active field of research in recent years. However, most published methods either analyse an entire video and assign it a single action label, or use relatively large look-ahead to classify each frame. Contrary to these strategies, human vision proves that simple actions can be recognised almost instantaneously. In this paper, we present a system for action recognition from very short sequences (ldquosnippetsrdquo) of 1-10 frames, and systematically evaluate it on standard data sets. It turns out that even local shape and optic flow for a single frame are enough to achieve ap90% correct recognitions, and snippets of 5-7 frames (0.3-0.5 seconds of video) are enough to achieve a performance similar to the one obtainable with the entire video sequence.
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Cites
Histograms of Oriented Gradients for Human Detection
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Hierarchical models of object recognition in cortex
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Neural mechanisms for the recognition of biological movements
Giese, M. A., & Poggio, T. (2003) · Nature Reviews Neuroscience · doi:10.1038/nrn1057 · Cited by 955
Cited by
Hmdb: a large video database for human motion recognition
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., & Serre, T. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126543 · Cited by 3,965
A survey on vision-based human action recognition
Poppe, R. (2010) · Image and Vision Computing · doi:10.1016/j.imavis.2009.11.014 · Cited by 2,242
Slow feature analysis for human action recognition
Zhang, Z., & Tao, D. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.157 · Cited by 307
Facial Expression Recognition Using Facial Movement Features
Zhang, L., & Tjondronegoro, D. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.13 · Cited by 253
Metadata
- Authors
- Schindler, K., & van Gool, L.
- Venue
- 2008 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2008-06
- Pages
- 1-8
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2136853139
- Cited by
- 526
- References
- 6
- FWCI
- 33.12
- Type
- conference paper
- Language
- en
- Open access
- closed
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