The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
A Biologically Inspired System for Action Recognition
Open accessAbstract
We present a biologically-motivated system for the recognition of actions from video sequences. The approach builds on recent work on object recognition based on hierarchical feedforward architectures [25, 16, 20] and extends a neurobiological model of motion processing in the visual cortex [10]. The system consists of a hierarchy of spatio-temporal feature detectors of increasing complexity: an input sequence is first analyzed by an array of motion- direction sensitive units which, through a hierarchy of processing stages, lead to position-invariant spatio-temporal feature detectors. We experiment with different types of motion-direction sensitive units as well as different system architectures. As in [16], we find that sparse features in intermediate stages outperform dense ones and that using a simple feature selection approach leads to an efficient system that performs better with far fewer features. We test the approach on different publicly available action datasets, in all cases achieving the highest results reported to date.
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Cited by
Two-Stream Convolutional Networks for Action Recognition in Videos
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756
Hmdb: a large video database for human motion recognition
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Self-citationA survey on vision-based human action recognition
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Slow feature analysis for human action recognition
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Facial Expression Recognition Using Facial Movement Features
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Metadata
- Authors
- Jhuang, H., Serre, T., Wolf, L., & Poggio, T.
- Venue
- 2007 IEEE 11th International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2007
- Pages
- 1-8
- OpenAlex
- W2096691069
- Cited by
- 723
- References
- 9
- FWCI
- 41.88
- Type
- conference paper
- Language
- en
- Open access
- green
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