The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Efficient regression of general-activity human poses from depth images
Abstract
We present a new approach to general-activity human pose estimation from depth images, building on Hough forests. We extend existing techniques in several ways: real time prediction of multiple 3D joints, explicit learning of voting weights, vote compression to allow larger training sets, and a comparison of several decision-tree training objectives. Key aspects of our work include: regression directly from the raw depth image, without the use of an arbitrary intermediate representation; applicability to general motions (not constrained to particular activities) and the ability to localize occluded as well as visible body joints. Experimental results demonstrate that our method produces state of the art results on several data sets including the challenging MSRC-5000 pose estimation test set, at a speed of about 200 frames per second. Results on silhouettes suggest broader applicability to other imaging modalities.
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Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
The elements of statistical learning: Data mining, inference, and prediction
Franklin, J. (2005) · The Mathematical Intelligencer · doi:10.1007/bf02985802 · Cited by 1,400
Cited by
Criminisi, A., Shotton, J., & Konukoglu, E. (2012) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000035 · Cited by 677
Self-citationAccurate, Robust, and Flexible Real-time Hand Tracking
Sharp, T., Keskin, C., Robertson, D., Taylor, J., Shotton, J., Kim, D., Rhemann, C., Leichter, I., Vinnikov, A., Wei, Y., et al. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702179 · Cited by 431
Self-citationLatent Regression Forest: Structured Estimation of 3D Articulated Hand Posture
Tang, D., Chang, H. J., Tejani, A., & Kim, T.-K. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.490 · Cited by 398
Robust human activity recognition from depth video using spatiotemporal multi-fused features
Jalal, A., Kim, Y.-H., Kim, Y.-J., Kamal, S., & Kim, D. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.08.003 · Cited by 384
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
Metadata
- Authors
- Girshick, R., Shotton, J., Kohli, P., Criminisi, A., & Fitzgibbon, A.
- Venue
- 2011 International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2011-11
- Pages
- 415-422
- OpenAlex
- W2000205118
- Cited by
- 344
- References
- 3
- FWCI
- 34.20
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Girshick, R., Shotton, J., Kohli, P., Criminisi, A., & Fitzgibbon, A. (2011). Efficient regression of general-activity human poses from depth images. 2011 International Conference on Computer Vision. https://doi.org/10.1109/iccv.2011.6126270
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