Video Person Re-ID: Fantastic Techniques and Where to Find Them (Student Abstract)

Priyank Pathak, Amir Erfan Eshratifar, Michael Gormish

AAAI 2020 pp. 13893-13894

doi:10.1609/AAAI.V34I10.7219 /aaai/2020/pathak2020aaai-video/

Abstract

The ability to identify the same person from multiple camera views without the explicit use of facial recognition is receiving commercial and academic interest. The current status-quo solutions are based on attention neural models. In this paper, we propose Attention and CL loss, which is a hybrid of center and Online Soft Mining (OSM) loss added to the attention loss on top of a temporal attention-based neural network. The proposed loss function applied with bag-of-tricks for training surpasses the state of the art on the common person Re-ID datasets, MARS and PRID 2011. Our source code is publicly available on github1.

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Cite

Text

Pathak et al. "Video Person Re-ID: Fantastic Techniques and Where to Find Them (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7219

Markdown

[Pathak et al. "Video Person Re-ID: Fantastic Techniques and Where to Find Them (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/pathak2020aaai-video/) doi:10.1609/AAAI.V34I10.7219

BibTeX

@inproceedings{pathak2020aaai-video,
  title     = {{Video Person Re-ID: Fantastic Techniques and Where to Find Them (Student Abstract)}},
  author    = {Pathak, Priyank and Eshratifar, Amir Erfan and Gormish, Michael},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {13893-13894},
  doi       = {10.1609/AAAI.V34I10.7219},
  url       = {https://mlanthology.org/aaai/2020/pathak2020aaai-video/}
}