Using K-Poselets for Detecting People and Localizing Their Keypoints
Abstract
A k-poselet is a deformable part model (DPM) with k parts, where each of the parts is a poselet, aligned to a specific configuration of keypoints based on ground-truth annotations. A separate template is used to learn the appearance of each part. The parts are allowed to move with respect to each other with a deformation cost that is learned at training time. This model is richer than both the traditional version of poselets and DPMs. It enables a unified approach to person detection and keypoint prediction which, barring contemporaneous approaches based on CNN features, achieves state-of-the-art keypoint prediction while maintaining competitive detection performance.
Cite
Text
Gkioxari et al. "Using K-Poselets for Detecting People and Localizing Their Keypoints." Conference on Computer Vision and Pattern Recognition, 2014. doi:10.1109/CVPR.2014.458Markdown
[Gkioxari et al. "Using K-Poselets for Detecting People and Localizing Their Keypoints." Conference on Computer Vision and Pattern Recognition, 2014.](https://mlanthology.org/cvpr/2014/gkioxari2014cvpr-using/) doi:10.1109/CVPR.2014.458BibTeX
@inproceedings{gkioxari2014cvpr-using,
title = {{Using K-Poselets for Detecting People and Localizing Their Keypoints}},
author = {Gkioxari, Georgia and Hariharan, Bharath and Girshick, Ross and Malik, Jitendra},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2014},
doi = {10.1109/CVPR.2014.458},
url = {https://mlanthology.org/cvpr/2014/gkioxari2014cvpr-using/}
}