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G3D: A gaming action dataset and real time action recognition evaluation framework
Abstract
In this paper a novel evaluation framework for measuring the performance of real-time action recognition methods is presented. The evaluation framework will extend the time-based event detection metric to model multiple distinct action classes. The proposed metric provides more accurate indications of the performance of action recognition algorithms for games and other similar applications since it takes into consideration restrictions related to time and consecutive repetitions. Furthermore, a new dataset, G3D for real-time action recognition in gaming containing synchronised video, depth and skeleton data is provided. Our results indicate the need of an advanced metric especially designed for games and other similar real-time applications.
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Citations
Cited by
Skeleton Optical Spectra-Based Action Recognition Using Convolutional Neural Networks
Hou, Y., Li, Z., Wang, P., & Li, W. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2016.2628339 · Cited by 339
Space-time representation of people based on 3D skeletal data: A review
Han, F., Reily, B., Hoff, W., & Zhang, H. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.01.011 · Cited by 316
RGB-D-based action recognition datasets: A survey
Zhang, J., Li, W., Ogunbona, P. O., Wang, P., & Tang, C. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.05.019 · Cited by 283
Multi-Modality Multi-Task Recurrent Neural Network for Online Action Detection
Liu, J., Li, Y., Song, S., Xing, J., Lan, C., & Zeng, W. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2799968 · Cited by 62
Metadata
- Authors
- Bloom, V., Makris, D., & Argyriou, V.
- Venue
- 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 7-12
- ISSN
- 2160-7508 (ISSN-L)
- OpenAlex
- W2016240821
- Cited by
- 221
- References
- 0
- FWCI
- 7.07
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: G3D: A gaming action dataset and real time action recognition evaluation framework. https://app.cochat.ai/papers/10.1109/cvprw.2012.6239175
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- Bloom, V., Makris, D., & Argyriou, V. (2012). G3D: A gaming action dataset and real time action recognition evaluation framework. 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. https://doi.org/10.1109/cvprw.2012.6239175
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