ML Anthology

Sparse Re-Id: Block Sparsity for Person Re-Identification

Srikrishna Karanam, Yang Li, Richard J. Radke
CVPRW 2015 pp. 33-40
doi:10.1109/CVPRW.2015.7301392 /cvprw/2015/karanam2015cvprw-sparse/

Abstract

This paper presents a novel approach to solve the problem of person re-identification in non-overlapping camera views. We hypothesize that the feature vector of a probe image approximately lies in the linear span of the corresponding gallery feature vectors in a learned embedding space. We then formulate the re-identification problem as a block sparse recovery problem and solve the associated optimization problem using the alternating directions framework. We evaluate our approach on the publicly available PRID 2011 and iLIDS-VID multi-shot re-identification datasets and demonstrate superior performance in comparison with the current state of the art.

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Cite

Text

Karanam et al. "Sparse Re-Id: Block Sparsity for Person Re-Identification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2015. doi:10.1109/CVPRW.2015.7301392

Markdown

[Karanam et al. "Sparse Re-Id: Block Sparsity for Person Re-Identification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2015.](https://mlanthology.org/cvprw/2015/karanam2015cvprw-sparse/) doi:10.1109/CVPRW.2015.7301392

BibTeX

@inproceedings{karanam2015cvprw-sparse,
  title     = {{Sparse Re-Id: Block Sparsity for Person Re-Identification}},
  author    = {Karanam, Srikrishna and Li, Yang and Radke, Richard J.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2015},
  pages     = {33-40},
  doi       = {10.1109/CVPRW.2015.7301392},
  url       = {https://mlanthology.org/cvprw/2015/karanam2015cvprw-sparse/}
}