ML Anthology

Human Activity Recognition Based on R Transform

Ying Wang, Kaiqi Huang, Tieniu Tan
CVPR 2007
doi:10.1109/CVPR.2007.383505 /cvpr/2007/wang2007cvpr-human/

Abstract

This paper addresses human activity recognition based on a new feature descriptor. For a binary human silhouette, an extended radon transform, R transform, is employed to represent low-level features. The advantage of the R transform lies in its low computational complexity and geometric invariance. Then a set of HMMs based on the extracted features are trained to recognize activities. Compared with other commonly-used feature descriptors, R transform is robust to frame loss in video, disjoint silhouettes and holes in the shape, and thus achieves better performance in recognizing similar activities. Rich experiments have proved the efficiency of the proposed method.

PDF CVPR Semantic Scholar

Cite

Text

Wang et al. "Human Activity Recognition Based on R Transform." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007. doi:10.1109/CVPR.2007.383505

Markdown

[Wang et al. "Human Activity Recognition Based on R Transform." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007.](https://mlanthology.org/cvpr/2007/wang2007cvpr-human/) doi:10.1109/CVPR.2007.383505

BibTeX

@inproceedings{wang2007cvpr-human,
  title     = {{Human Activity Recognition Based on R Transform}},
  author    = {Wang, Ying and Huang, Kaiqi and Tan, Tieniu},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2007},
  doi       = {10.1109/CVPR.2007.383505},
  url       = {https://mlanthology.org/cvpr/2007/wang2007cvpr-human/}
}