Rotation angle recovery for rotation invariant detector in lying pose human body detection

Dao-Xun Xia; Shao-Zi Li

Rotation angle recovery for rotation invariant detector in lying pose human body detection

View Fulltext

Author(s): Dao-Xun Xia^{1, 2} and Shao-Zi Li^{1, 2}
- Affiliations: 1: School of Information Science and Technology , Xiamen University , Xiamen 361005 , People's Republic of China ;
  2: School of Mathematics and Computer Science , Guizhou Normal University , Guiyang 550001 , People's Republic of China
Source: Volume 2015, Issue 4, April 2015, p. 160 – 163
DOI: 10.1049/joe.2015.0032 , Online ISSN 2051-3305

This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/3.0/)

Received 02/03/2015, Accepted 04/03/2015, Published 12/03/2015

A method for rotation-invariant lying-pose human body detection in overlooking images is proposed. The rotation-invariant histogram of oriented gradient using Fourier analysis in polar coordinate is exploited as descriptor for lying-pose human body. And then the authors used the exhaustive sliding window search strategy with multiple scale scan to localise human body. Finally, principal component analysis (PCA) is used to determine the rotation angle of the exhaustive sliding window based on the classifier output scores. Experiments on their built XiaMen University Lying-Pose Dataset (XMULP) show the effectiveness of their proposed method.

References

1. 1)
  - 26. Pedro, F.F., Ross, B.G., David, M., et al: ‘Object detection with discriminatively trained part-based models’, IEEE Trans. Pattern Anal. Mach. Intell., 2010, 32, (9), pp. 1627–1645 (doi: 10.1109/TPAMI.2009.167).
2. 2)
  - 3. Liu, K., Skibbe, H., Schmidt, T., Blein, T., Palme, K., Brox, T., et al: ‘Rotation-invariant HOG descriptors using Fourier analysis in polar and spherical coordinates’, Phys. IJCV, 2014, 106, (3), pp. 342–364 (doi: 10.1007/s11263-013-0634-z).
3. 3)
  - 7. Dollár, P., Appel, R., Belongie, S., Perona, P.: ‘Fast feature pyramids for object detection’, Phys. PAMI, 2014, 36, (8), pp. 1532–1545 (doi: 10.1109/TPAMI.2014.2300479).
4. 4)
  - 1. Takacs, G., Chandrasekhar, V., Tsai, S.S., Chen, D., Grzeszczuk, R., Girod, B.: ‘Fast computation of rotation-invariant image features by an approximate radial gradient transform’, Phys. TIP, 2013, 22, (8), pp. 2970–2982.
5. 5)
  - 4. Xia, D.X., Su, S.Z., Li, S.Z., Jodoin, P.M.: ‘Lying-pose detection with training dataset expansion’. ICIP, October 2014.
6. 6)
  - P.F. Felzenszwalb , R.B. Girshick , D. McAllester , D. Ramanan . Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. , 9 , 1627 - 1645
7. 7)
  - 3. Liu, K., Skibbe, H., Schmidt, T., Blein, T., Palme, K., Brox, T., et al: ‘Rotation-invariant HOG descriptors using Fourier analysis in polar and spherical coordinates’, Phys. IJCV, 2014, 106, (3), pp. 342–364 (doi: 10.1007/s11263-013-0634-z).
8. 8)
  - 5. Dalal, N., Triggs, B.: ‘Histograms of oriented gradients for human detection’. CVPR, June 2005, vol. 2, pp. 886–893.
9. 9)
  - 2. Wang, L., Wu, C.D., Chen, D.Y., Lu, B.H.: ‘Rotation invariant human detection scheme based on polar-hogs feature and double scale direction estimation’, Phys. SOPO, 2011, pp. 1–4.
10. 10)
  - 7. Dollár, P., Appel, R., Belongie, S., Perona, P.: ‘Fast feature pyramids for object detection’, Phys. PAMI, 2014, 36, (8), pp. 1532–1545 (doi: 10.1109/TPAMI.2014.2300479).

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

Rotation angle recovery for rotation invariant detector in lying pose human body detection

References

Related content