Training Deformable Object Models for Human Detection Based on Alignment and Clustering
Abstract
We propose a clustering method that considers non-rigid alignment of samples. The motivation for such a clustering is training of object detectors that consist of multiple mixture components. In particular, we consider the deformable part model (DPM) of Felzenszwalb et al., where each mixture component includes a learned deformation model. We show that alignment based clustering distributes the data better to the mixture components of the DPM than previous methods. Moreover, the alignment helps the non-convex optimization of the DPM find a consistent placement of its parts and, thus, learn more accurate part filters.
Cite
Text
Drayer and Brox. "Training Deformable Object Models for Human Detection Based on Alignment and Clustering." European Conference on Computer Vision, 2014. doi:10.1007/978-3-319-10602-1_27Markdown
[Drayer and Brox. "Training Deformable Object Models for Human Detection Based on Alignment and Clustering." European Conference on Computer Vision, 2014.](https://mlanthology.org/eccv/2014/drayer2014eccv-training/) doi:10.1007/978-3-319-10602-1_27BibTeX
@inproceedings{drayer2014eccv-training,
title = {{Training Deformable Object Models for Human Detection Based on Alignment and Clustering}},
author = {Drayer, Benjamin and Brox, Thomas},
booktitle = {European Conference on Computer Vision},
year = {2014},
pages = {406-420},
doi = {10.1007/978-3-319-10602-1_27},
url = {https://mlanthology.org/eccv/2014/drayer2014eccv-training/}
}